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[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
func main<ios16>(tensor<fp16, [1, 4, 64, 128]> z) {
tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_1_strides_0 = const()[name = tensor<string, []>("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_1_dilations_0 = const()[name = tensor<string, []>("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_1_groups_0 = const()[name = tensor<string, []>("input_1_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [4, 4, 1, 1]> post_quant_conv_weight_to_fp16 = const()[name = tensor<string, []>("post_quant_conv_weight_to_fp16"), val = tensor<fp16, [4, 4, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<fp16, [4]> post_quant_conv_bias_to_fp16 = const()[name = tensor<string, []>("post_quant_conv_bias_to_fp16"), val = tensor<fp16, [4]>([-0x1.a6p-6, -0x1.9f4p-4, -0x1.b58p-3, 0x1.7fp-3])];
tensor<fp16, [1, 4, 64, 128]> input_1_cast_fp16 = conv(bias = post_quant_conv_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = post_quant_conv_weight_to_fp16, x = z)[name = tensor<string, []>("input_1_cast_fp16")];
tensor<int32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<int32, []>(-1)];
tensor<int32, [4]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [4]>([1, 4, 64, 128])];
tensor<bool, [4]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 4, 64, 1]> var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = input_1_cast_fp16)[name = tensor<string, []>("op_46_cast_fp16")];
tensor<int32, [4]> var_47_begin_0 = const()[name = tensor<string, []>("op_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_47_end_0 = const()[name = tensor<string, []>("op_47_end_0"), val = tensor<int32, [4]>([1, 4, 64, 1])];
tensor<bool, [4]> var_47_end_mask_0 = const()[name = tensor<string, []>("op_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 4, 64, 1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, x = input_1_cast_fp16)[name = tensor<string, []>("op_47_cast_fp16")];
tensor<bool, []> input_3_interleave_0 = const()[name = tensor<string, []>("input_3_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 4, 64, 130]> input_3_cast_fp16 = concat(axis = var_24, interleave = input_3_interleave_0, values = (var_46_cast_fp16, input_1_cast_fp16, var_47_cast_fp16))[name = tensor<string, []>("input_3_cast_fp16")];
tensor<int32, [8]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 4, 66, 130]> input_5_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 4, 3, 3]> decoder_conv_in_weight_to_fp16 = const()[name = tensor<string, []>("decoder_conv_in_weight_to_fp16"), val = tensor<fp16, [512, 4, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192)))];
tensor<fp16, [512]> decoder_conv_in_bias_to_fp16 = const()[name = tensor<string, []>("decoder_conv_in_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37120)))];
tensor<fp16, [1, 512, 64, 128]> input_7_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = decoder_conv_in_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16)[name = tensor<string, []>("reshape_0_cast_fp16")];
tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_0_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_2_cast_fp16")];
tensor<fp16, []> add_0_y_0_to_fp16 = const()[name = tensor<string, []>("add_0_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor<string, []>("add_0_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor<string, []>("real_div_0_cast_fp16")];
tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor<string, []>("reshape_1_cast_fp16")];
tensor<fp16, [512]> add_1_mean_0_to_fp16 = const()[name = tensor<string, []>("add_1_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38208)))];
tensor<fp16, [512]> add_1_variance_0_to_fp16 = const()[name = tensor<string, []>("add_1_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39296)))];
tensor<fp16, [512]> add_1_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_1_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40384)))];
tensor<fp16, [512]> add_1_beta_0_to_fp16 = const()[name = tensor<string, []>("add_1_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41472)))];
tensor<fp16, []> add_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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<string, []>("add_1_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
tensor<int32, [4]> var_73_begin_0 = const()[name = tensor<string, []>("op_73_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_73_end_0 = const()[name = tensor<string, []>("op_73_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_73_end_mask_0 = const()[name = tensor<string, []>("op_73_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_73_cast_fp16 = slice_by_index(begin = var_73_begin_0, end = var_73_end_0, end_mask = var_73_end_mask_0, x = input_11_cast_fp16)[name = tensor<string, []>("op_73_cast_fp16")];
tensor<int32, [4]> var_74_begin_0 = const()[name = tensor<string, []>("op_74_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_74_end_0 = const()[name = tensor<string, []>("op_74_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_74_end_mask_0 = const()[name = tensor<string, []>("op_74_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_74_cast_fp16 = slice_by_index(begin = var_74_begin_0, end = var_74_end_0, end_mask = var_74_end_mask_0, x = input_11_cast_fp16)[name = tensor<string, []>("op_74_cast_fp16")];
tensor<bool, []> input_13_interleave_0 = const()[name = tensor<string, []>("input_13_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_13_cast_fp16 = concat(axis = var_24, interleave = input_13_interleave_0, values = (var_73_cast_fp16, input_11_cast_fp16, var_74_cast_fp16))[name = tensor<string, []>("input_13_cast_fp16")];
tensor<int32, [8]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_15_mode_0 = const()[name = tensor<string, []>("input_15_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_15_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_15_mode_0, pad = input_15_pad_0, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42560)))];
tensor<fp16, [512]> decoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4761216)))];
tensor<fp16, [1, 512, 64, 128]> input_17_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor<string, []>("reshape_4_cast_fp16")];
tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_3_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor<string, []>("sub_2_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_5_cast_fp16")];
tensor<fp16, []> add_2_y_0_to_fp16 = const()[name = tensor<string, []>("add_2_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor<string, []>("add_2_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor<string, []>("sqrt_1_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor<string, []>("real_div_1_cast_fp16")];
tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor<string, []>("reshape_5_cast_fp16")];
tensor<fp16, [512]> add_3_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_3_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4762304)))];
tensor<fp16, [512]> add_3_beta_0_to_fp16 = const()[name = tensor<string, []>("add_3_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4763392)))];
tensor<fp16, []> add_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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<string, []>("add_3_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
tensor<int32, [4]> var_91_begin_0 = const()[name = tensor<string, []>("op_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_91_end_0 = const()[name = tensor<string, []>("op_91_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_91_end_mask_0 = const()[name = tensor<string, []>("op_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_91_cast_fp16 = slice_by_index(begin = var_91_begin_0, end = var_91_end_0, end_mask = var_91_end_mask_0, x = input_21_cast_fp16)[name = tensor<string, []>("op_91_cast_fp16")];
tensor<int32, [4]> var_92_begin_0 = const()[name = tensor<string, []>("op_92_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_92_end_0 = const()[name = tensor<string, []>("op_92_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_92_end_mask_0 = const()[name = tensor<string, []>("op_92_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_92_cast_fp16 = slice_by_index(begin = var_92_begin_0, end = var_92_end_0, end_mask = var_92_end_mask_0, x = input_21_cast_fp16)[name = tensor<string, []>("op_92_cast_fp16")];
tensor<bool, []> input_25_interleave_0 = const()[name = tensor<string, []>("input_25_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_25_cast_fp16 = concat(axis = var_24, interleave = input_25_interleave_0, values = (var_91_cast_fp16, input_21_cast_fp16, var_92_cast_fp16))[name = tensor<string, []>("input_25_cast_fp16")];
tensor<int32, [8]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_27_mode_0 = const()[name = tensor<string, []>("input_27_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_27_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_27_mode_0, pad = input_27_pad_0, x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_1_strides_0 = const()[name = tensor<string, []>("hidden_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_1_dilations_0 = const()[name = tensor<string, []>("hidden_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_1_groups_0 = const()[name = tensor<string, []>("hidden_states_1_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4764480)))];
tensor<fp16, [512]> decoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9483136)))];
tensor<fp16, [1, 512, 64, 128]> hidden_states_1_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_1_dilations_0, groups = hidden_states_1_groups_0, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = hidden_states_1_strides_0, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("hidden_states_1_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> var_102_cast_fp16 = add(x = input_7_cast_fp16, y = hidden_states_1_cast_fp16)[name = tensor<string, []>("op_102_cast_fp16")];
tensor<int32, [4]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 8192])];
tensor<fp16, [1, 32, 16, 8192]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_102_cast_fp16)[name = tensor<string, []>("reshape_8_cast_fp16")];
tensor<int32, [2]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [2]>([2, 3])];
tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1]> 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<string, []>("reduce_mean_6_cast_fp16")];
tensor<fp16, [1, 32, 16, 8192]> sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor<string, []>("sub_4_cast_fp16")];
tensor<fp16, [1, 32, 16, 8192]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])];
tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1]> 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<string, []>("reduce_mean_8_cast_fp16")];
tensor<fp16, []> add_4_y_0_to_fp16 = const()[name = tensor<string, []>("add_4_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor<string, []>("add_4_cast_fp16")];
tensor<fp16, [1, 32, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor<string, []>("sqrt_2_cast_fp16")];
tensor<fp16, [1, 32, 16, 8192]> real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor<string, []>("real_div_2_cast_fp16")];
tensor<int32, [3]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 512, 8192])];
tensor<fp16, [1, 512, 8192]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor<string, []>("reshape_9_cast_fp16")];
tensor<fp16, [1, 512, 1]> reshape_10_to_fp16 = const()[name = tensor<string, []>("reshape_10_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9484224)))];
tensor<fp16, [1, 512, 8192]> mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor<string, []>("mul_2_cast_fp16")];
tensor<fp16, [1, 512, 1]> reshape_11_to_fp16 = const()[name = tensor<string, []>("reshape_11_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9485312)))];
tensor<fp16, [1, 512, 8192]> add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
tensor<int32, [3]> input_31_perm_0 = const()[name = tensor<string, []>("input_31_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9486400)))];
tensor<fp16, [512]> decoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10010752)))];
tensor<fp16, [1, 8192, 512]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = add_5_cast_fp16)[name = tensor<string, []>("transpose_11")];
tensor<fp16, [1, 8192, 512]> linear_0_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_q_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10011840)))];
tensor<fp16, [512]> decoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10536192)))];
tensor<fp16, [1, 8192, 512]> linear_1_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_k_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10537280)))];
tensor<fp16, [512]> decoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11061632)))];
tensor<fp16, [1, 8192, 512]> linear_2_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_v_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
tensor<int32, [4]> var_143 = const()[name = tensor<string, []>("op_143"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 8192, 1, 512]> var_144_cast_fp16 = reshape(shape = var_143, x = linear_0_cast_fp16)[name = tensor<string, []>("op_144_cast_fp16")];
tensor<int32, [4]> var_146 = const()[name = tensor<string, []>("op_146"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 8192, 1, 512]> var_147_cast_fp16 = reshape(shape = var_146, x = linear_1_cast_fp16)[name = tensor<string, []>("op_147_cast_fp16")];
tensor<int32, [4]> var_149 = const()[name = tensor<string, []>("op_149"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 8192, 1, 512]> var_150_cast_fp16 = reshape(shape = var_149, x = linear_2_cast_fp16)[name = tensor<string, []>("op_150_cast_fp16")];
tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, []> mul_3_y_0_to_fp16 = const()[name = tensor<string, []>("mul_3_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-5)];
tensor<fp16, [1, 8192, 1, 512]> mul_3_cast_fp16 = mul(x = var_144_cast_fp16, y = mul_3_y_0_to_fp16)[name = tensor<string, []>("mul_3_cast_fp16")];
tensor<bool, []> matmul_0_transpose_y_0 = const()[name = tensor<string, []>("matmul_0_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<bool, []> matmul_0_transpose_x_0 = const()[name = tensor<string, []>("matmul_0_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<int32, [4]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<fp16, [1, 1, 8192, 512]> transpose_5 = transpose(perm = transpose_5_perm_0, x = var_147_cast_fp16)[name = tensor<string, []>("transpose_8")];
tensor<fp16, [1, 1, 8192, 512]> transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_3_cast_fp16)[name = tensor<string, []>("transpose_9")];
tensor<fp16, [1, 1, 8192, 8192]> matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_4, y = transpose_5)[name = tensor<string, []>("matmul_0_cast_fp16")];
tensor<int32, []> softmax_0_axis_0 = const()[name = tensor<string, []>("softmax_0_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 1, 8192, 8192]> softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = matmul_0_cast_fp16)[name = tensor<string, []>("softmax_0_cast_fp16")];
tensor<bool, []> hidden_states_7_transpose_x_0 = const()[name = tensor<string, []>("hidden_states_7_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> hidden_states_7_transpose_y_0 = const()[name = tensor<string, []>("hidden_states_7_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 1, 8192, 512]> value_cast_fp16 = transpose(perm = value_perm_0, x = var_150_cast_fp16)[name = tensor<string, []>("transpose_10")];
tensor<fp16, [1, 1, 8192, 512]> hidden_states_7_cast_fp16 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0_cast_fp16, y = value_cast_fp16)[name = tensor<string, []>("hidden_states_7_cast_fp16")];
tensor<int32, [4]> var_153_perm_0 = const()[name = tensor<string, []>("op_153_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_157 = const()[name = tensor<string, []>("op_157"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp16, [1, 8192, 1, 512]> var_153_cast_fp16 = transpose(perm = var_153_perm_0, x = hidden_states_7_cast_fp16)[name = tensor<string, []>("transpose_7")];
tensor<fp16, [1, 8192, 512]> hidden_states_9_cast_fp16 = reshape(shape = var_157, x = var_153_cast_fp16)[name = tensor<string, []>("hidden_states_9_cast_fp16")];
tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11062720)))];
tensor<fp16, [512]> decoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11587072)))];
tensor<fp16, [1, 8192, 512]> linear_3_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
tensor<int32, [3]> var_164_perm_0 = const()[name = tensor<string, []>("op_164_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
tensor<int32, [4]> var_165 = const()[name = tensor<string, []>("op_165"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 8192]> var_164_cast_fp16 = transpose(perm = var_164_perm_0, x = linear_3_cast_fp16)[name = tensor<string, []>("transpose_6")];
tensor<fp16, [1, 512, 64, 128]> hidden_states_13_cast_fp16 = reshape(shape = var_165, x = var_164_cast_fp16)[name = tensor<string, []>("hidden_states_13_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> hidden_states_15_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_102_cast_fp16)[name = tensor<string, []>("hidden_states_15_cast_fp16")];
tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = hidden_states_15_cast_fp16)[name = tensor<string, []>("reshape_12_cast_fp16")];
tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_9_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor<string, []>("sub_6_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor<string, []>("square_3_cast_fp16")];
tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_11_cast_fp16")];
tensor<fp16, []> add_6_y_0_to_fp16 = const()[name = tensor<string, []>("add_6_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor<string, []>("sqrt_3_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor<string, []>("real_div_3_cast_fp16")];
tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor<string, []>("reshape_13_cast_fp16")];
tensor<fp16, [512]> add_7_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_7_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11588160)))];
tensor<fp16, [512]> add_7_beta_0_to_fp16 = const()[name = tensor<string, []>("add_7_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11589248)))];
tensor<fp16, []> add_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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<string, []>("add_7_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_41_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
tensor<int32, [4]> var_180_begin_0 = const()[name = tensor<string, []>("op_180_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_180_end_0 = const()[name = tensor<string, []>("op_180_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_180_end_mask_0 = const()[name = tensor<string, []>("op_180_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_180_cast_fp16 = slice_by_index(begin = var_180_begin_0, end = var_180_end_0, end_mask = var_180_end_mask_0, x = input_41_cast_fp16)[name = tensor<string, []>("op_180_cast_fp16")];
tensor<int32, [4]> var_181_begin_0 = const()[name = tensor<string, []>("op_181_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_181_end_0 = const()[name = tensor<string, []>("op_181_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_181_end_mask_0 = const()[name = tensor<string, []>("op_181_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_181_cast_fp16 = slice_by_index(begin = var_181_begin_0, end = var_181_end_0, end_mask = var_181_end_mask_0, x = input_41_cast_fp16)[name = tensor<string, []>("op_181_cast_fp16")];
tensor<bool, []> input_43_interleave_0 = const()[name = tensor<string, []>("input_43_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_43_cast_fp16 = concat(axis = var_24, interleave = input_43_interleave_0, values = (var_180_cast_fp16, input_41_cast_fp16, var_181_cast_fp16))[name = tensor<string, []>("input_43_cast_fp16")];
tensor<int32, [8]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_45_mode_0 = const()[name = tensor<string, []>("input_45_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_45_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_45_mode_0, pad = input_45_pad_0, x = input_43_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
tensor<string, []> input_47_pad_type_0 = const()[name = tensor<string, []>("input_47_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_47_strides_0 = const()[name = tensor<string, []>("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_47_pad_0 = const()[name = tensor<string, []>("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_47_dilations_0 = const()[name = tensor<string, []>("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_47_groups_0 = const()[name = tensor<string, []>("input_47_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11590336)))];
tensor<fp16, [512]> decoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16308992)))];
tensor<fp16, [1, 512, 64, 128]> input_47_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_47_cast_fp16)[name = tensor<string, []>("reshape_16_cast_fp16")];
tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = tensor<string, []>("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_12_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_12_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_12_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor<string, []>("sub_8_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor<string, []>("square_4_cast_fp16")];
tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = tensor<string, []>("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_14_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_14_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_14_cast_fp16")];
tensor<fp16, []> add_8_y_0_to_fp16 = const()[name = tensor<string, []>("add_8_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor<string, []>("sqrt_4_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor<string, []>("real_div_4_cast_fp16")];
tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor<string, []>("reshape_17_cast_fp16")];
tensor<fp16, [512]> add_9_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_9_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16310080)))];
tensor<fp16, [512]> add_9_beta_0_to_fp16 = const()[name = tensor<string, []>("add_9_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16311168)))];
tensor<fp16, []> add_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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<string, []>("add_9_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_51_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
tensor<int32, [4]> var_198_begin_0 = const()[name = tensor<string, []>("op_198_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_198_end_0 = const()[name = tensor<string, []>("op_198_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_198_end_mask_0 = const()[name = tensor<string, []>("op_198_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_198_cast_fp16 = slice_by_index(begin = var_198_begin_0, end = var_198_end_0, end_mask = var_198_end_mask_0, x = input_51_cast_fp16)[name = tensor<string, []>("op_198_cast_fp16")];
tensor<int32, [4]> var_199_begin_0 = const()[name = tensor<string, []>("op_199_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_199_end_0 = const()[name = tensor<string, []>("op_199_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_199_end_mask_0 = const()[name = tensor<string, []>("op_199_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_199_cast_fp16 = slice_by_index(begin = var_199_begin_0, end = var_199_end_0, end_mask = var_199_end_mask_0, x = input_51_cast_fp16)[name = tensor<string, []>("op_199_cast_fp16")];
tensor<bool, []> input_55_interleave_0 = const()[name = tensor<string, []>("input_55_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_55_cast_fp16 = concat(axis = var_24, interleave = input_55_interleave_0, values = (var_198_cast_fp16, input_51_cast_fp16, var_199_cast_fp16))[name = tensor<string, []>("input_55_cast_fp16")];
tensor<int32, [8]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_57_mode_0 = const()[name = tensor<string, []>("input_57_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_57_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_57_mode_0, pad = input_57_pad_0, x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = tensor<string, []>("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = tensor<string, []>("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_17_groups_0 = const()[name = tensor<string, []>("hidden_states_17_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16312256)))];
tensor<fp16, [512]> decoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21030912)))];
tensor<fp16, [1, 512, 64, 128]> hidden_states_17_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("hidden_states_17_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> var_209_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = tensor<string, []>("op_209_cast_fp16")];
tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_209_cast_fp16)[name = tensor<string, []>("reshape_20_cast_fp16")];
tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_15_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor<string, []>("sub_10_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor<string, []>("square_5_cast_fp16")];
tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_17_cast_fp16")];
tensor<fp16, []> add_10_y_0_to_fp16 = const()[name = tensor<string, []>("add_10_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor<string, []>("add_10_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor<string, []>("sqrt_5_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor<string, []>("real_div_5_cast_fp16")];
tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor<string, []>("reshape_21_cast_fp16")];
tensor<fp16, [512]> add_11_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_11_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21032000)))];
tensor<fp16, [512]> add_11_beta_0_to_fp16 = const()[name = tensor<string, []>("add_11_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21033088)))];
tensor<fp16, []> add_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor<string, []>("add_11_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_63_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
tensor<int32, [4]> var_231_begin_0 = const()[name = tensor<string, []>("op_231_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_231_end_0 = const()[name = tensor<string, []>("op_231_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_231_end_mask_0 = const()[name = tensor<string, []>("op_231_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_231_cast_fp16 = slice_by_index(begin = var_231_begin_0, end = var_231_end_0, end_mask = var_231_end_mask_0, x = input_63_cast_fp16)[name = tensor<string, []>("op_231_cast_fp16")];
tensor<int32, [4]> var_232_begin_0 = const()[name = tensor<string, []>("op_232_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_232_end_0 = const()[name = tensor<string, []>("op_232_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_232_end_mask_0 = const()[name = tensor<string, []>("op_232_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_232_cast_fp16 = slice_by_index(begin = var_232_begin_0, end = var_232_end_0, end_mask = var_232_end_mask_0, x = input_63_cast_fp16)[name = tensor<string, []>("op_232_cast_fp16")];
tensor<bool, []> input_65_interleave_0 = const()[name = tensor<string, []>("input_65_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_65_cast_fp16 = concat(axis = var_24, interleave = input_65_interleave_0, values = (var_231_cast_fp16, input_63_cast_fp16, var_232_cast_fp16))[name = tensor<string, []>("input_65_cast_fp16")];
tensor<int32, [8]> input_67_pad_0 = const()[name = tensor<string, []>("input_67_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_67_mode_0 = const()[name = tensor<string, []>("input_67_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_67_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21034176)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25752832)))];
tensor<fp16, [1, 512, 64, 128]> input_69_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_69_cast_fp16)[name = tensor<string, []>("reshape_24_cast_fp16")];
tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_18_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor<string, []>("sub_12_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor<string, []>("square_6_cast_fp16")];
tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_20_cast_fp16")];
tensor<fp16, []> add_12_y_0_to_fp16 = const()[name = tensor<string, []>("add_12_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor<string, []>("add_12_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor<string, []>("sqrt_6_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor<string, []>("real_div_6_cast_fp16")];
tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor<string, []>("reshape_25_cast_fp16")];
tensor<fp16, [512]> add_13_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_13_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25753920)))];
tensor<fp16, [512]> add_13_beta_0_to_fp16 = const()[name = tensor<string, []>("add_13_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25755008)))];
tensor<fp16, []> add_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor<string, []>("add_13_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_73_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
tensor<int32, [4]> var_249_begin_0 = const()[name = tensor<string, []>("op_249_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_249_end_0 = const()[name = tensor<string, []>("op_249_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_249_end_mask_0 = const()[name = tensor<string, []>("op_249_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_249_cast_fp16 = slice_by_index(begin = var_249_begin_0, end = var_249_end_0, end_mask = var_249_end_mask_0, x = input_73_cast_fp16)[name = tensor<string, []>("op_249_cast_fp16")];
tensor<int32, [4]> var_250_begin_0 = const()[name = tensor<string, []>("op_250_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_250_end_0 = const()[name = tensor<string, []>("op_250_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_250_end_mask_0 = const()[name = tensor<string, []>("op_250_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_250_cast_fp16 = slice_by_index(begin = var_250_begin_0, end = var_250_end_0, end_mask = var_250_end_mask_0, x = input_73_cast_fp16)[name = tensor<string, []>("op_250_cast_fp16")];
tensor<bool, []> input_77_interleave_0 = const()[name = tensor<string, []>("input_77_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_77_cast_fp16 = concat(axis = var_24, interleave = input_77_interleave_0, values = (var_249_cast_fp16, input_73_cast_fp16, var_250_cast_fp16))[name = tensor<string, []>("input_77_cast_fp16")];
tensor<int32, [8]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_79_mode_0 = const()[name = tensor<string, []>("input_79_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_12_to_fp16 = const()[name = tensor<string, []>("const_12_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_79_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_79_mode_0, pad = input_79_pad_0, x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = tensor<string, []>("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_19_dilations_0 = const()[name = tensor<string, []>("hidden_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_19_groups_0 = const()[name = tensor<string, []>("hidden_states_19_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25756096)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30474752)))];
tensor<fp16, [1, 512, 64, 128]> hidden_states_19_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("hidden_states_19_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> var_260_cast_fp16 = add(x = var_209_cast_fp16, y = hidden_states_19_cast_fp16)[name = tensor<string, []>("op_260_cast_fp16")];
tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_260_cast_fp16)[name = tensor<string, []>("reshape_28_cast_fp16")];
tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_21_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor<string, []>("sub_14_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor<string, []>("square_7_cast_fp16")];
tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_23_cast_fp16")];
tensor<fp16, []> add_14_y_0_to_fp16 = const()[name = tensor<string, []>("add_14_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor<string, []>("add_14_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor<string, []>("sqrt_7_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor<string, []>("real_div_7_cast_fp16")];
tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor<string, []>("reshape_29_cast_fp16")];
tensor<fp16, [512]> add_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_15_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30475840)))];
tensor<fp16, [512]> add_15_beta_0_to_fp16 = const()[name = tensor<string, []>("add_15_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30476928)))];
tensor<fp16, []> add_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_85_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
tensor<int32, [4]> var_273_begin_0 = const()[name = tensor<string, []>("op_273_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_273_end_0 = const()[name = tensor<string, []>("op_273_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_273_end_mask_0 = const()[name = tensor<string, []>("op_273_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_273_cast_fp16 = slice_by_index(begin = var_273_begin_0, end = var_273_end_0, end_mask = var_273_end_mask_0, x = input_85_cast_fp16)[name = tensor<string, []>("op_273_cast_fp16")];
tensor<int32, [4]> var_274_begin_0 = const()[name = tensor<string, []>("op_274_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_274_end_0 = const()[name = tensor<string, []>("op_274_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_274_end_mask_0 = const()[name = tensor<string, []>("op_274_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_274_cast_fp16 = slice_by_index(begin = var_274_begin_0, end = var_274_end_0, end_mask = var_274_end_mask_0, x = input_85_cast_fp16)[name = tensor<string, []>("op_274_cast_fp16")];
tensor<bool, []> input_87_interleave_0 = const()[name = tensor<string, []>("input_87_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_87_cast_fp16 = concat(axis = var_24, interleave = input_87_interleave_0, values = (var_273_cast_fp16, input_85_cast_fp16, var_274_cast_fp16))[name = tensor<string, []>("input_87_cast_fp16")];
tensor<int32, [8]> input_89_pad_0 = const()[name = tensor<string, []>("input_89_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_89_mode_0 = const()[name = tensor<string, []>("input_89_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_13_to_fp16 = const()[name = tensor<string, []>("const_13_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_89_cast_fp16 = pad(constant_val = const_13_to_fp16, mode = input_89_mode_0, pad = input_89_pad_0, x = input_87_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_91_strides_0 = const()[name = tensor<string, []>("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_91_dilations_0 = const()[name = tensor<string, []>("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_91_groups_0 = const()[name = tensor<string, []>("input_91_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30478016)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35196672)))];
tensor<fp16, [1, 512, 64, 128]> input_91_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_91_cast_fp16)[name = tensor<string, []>("reshape_32_cast_fp16")];
tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = tensor<string, []>("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_24_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_24_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_24_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor<string, []>("sub_16_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor<string, []>("square_8_cast_fp16")];
tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = tensor<string, []>("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_26_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_26_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_26_cast_fp16")];
tensor<fp16, []> add_16_y_0_to_fp16 = const()[name = tensor<string, []>("add_16_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor<string, []>("sqrt_8_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor<string, []>("real_div_8_cast_fp16")];
tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor<string, []>("reshape_33_cast_fp16")];
tensor<fp16, [512]> add_17_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_17_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35197760)))];
tensor<fp16, [512]> add_17_beta_0_to_fp16 = const()[name = tensor<string, []>("add_17_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35198848)))];
tensor<fp16, []> add_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_95_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
tensor<int32, [4]> var_291_begin_0 = const()[name = tensor<string, []>("op_291_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_291_end_0 = const()[name = tensor<string, []>("op_291_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_291_end_mask_0 = const()[name = tensor<string, []>("op_291_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_291_cast_fp16 = slice_by_index(begin = var_291_begin_0, end = var_291_end_0, end_mask = var_291_end_mask_0, x = input_95_cast_fp16)[name = tensor<string, []>("op_291_cast_fp16")];
tensor<int32, [4]> var_292_begin_0 = const()[name = tensor<string, []>("op_292_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_292_end_0 = const()[name = tensor<string, []>("op_292_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_292_end_mask_0 = const()[name = tensor<string, []>("op_292_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_292_cast_fp16 = slice_by_index(begin = var_292_begin_0, end = var_292_end_0, end_mask = var_292_end_mask_0, x = input_95_cast_fp16)[name = tensor<string, []>("op_292_cast_fp16")];
tensor<bool, []> input_99_interleave_0 = const()[name = tensor<string, []>("input_99_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_99_cast_fp16 = concat(axis = var_24, interleave = input_99_interleave_0, values = (var_291_cast_fp16, input_95_cast_fp16, var_292_cast_fp16))[name = tensor<string, []>("input_99_cast_fp16")];
tensor<int32, [8]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_101_mode_0 = const()[name = tensor<string, []>("input_101_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_101_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_101_mode_0, pad = input_101_pad_0, x = input_99_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
tensor<string, []> hidden_states_21_pad_type_0 = const()[name = tensor<string, []>("hidden_states_21_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_21_strides_0 = const()[name = tensor<string, []>("hidden_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = tensor<string, []>("hidden_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_21_dilations_0 = const()[name = tensor<string, []>("hidden_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_21_groups_0 = const()[name = tensor<string, []>("hidden_states_21_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35199936)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39918592)))];
tensor<fp16, [1, 512, 64, 128]> hidden_states_21_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_21_dilations_0, groups = hidden_states_21_groups_0, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = hidden_states_21_strides_0, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("hidden_states_21_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> var_302_cast_fp16 = add(x = var_260_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor<string, []>("op_302_cast_fp16")];
tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_302_cast_fp16)[name = tensor<string, []>("reshape_36_cast_fp16")];
tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_27_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor<string, []>("sub_18_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor<string, []>("square_9_cast_fp16")];
tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_29_cast_fp16")];
tensor<fp16, []> add_18_y_0_to_fp16 = const()[name = tensor<string, []>("add_18_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor<string, []>("sqrt_9_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor<string, []>("real_div_9_cast_fp16")];
tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor<string, []>("reshape_37_cast_fp16")];
tensor<fp16, [512]> add_19_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_19_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39919680)))];
tensor<fp16, [512]> add_19_beta_0_to_fp16 = const()[name = tensor<string, []>("add_19_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39920768)))];
tensor<fp16, []> add_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor<string, []>("add_19_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_107_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
tensor<int32, [4]> var_315_begin_0 = const()[name = tensor<string, []>("op_315_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_315_end_0 = const()[name = tensor<string, []>("op_315_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_315_end_mask_0 = const()[name = tensor<string, []>("op_315_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_315_cast_fp16 = slice_by_index(begin = var_315_begin_0, end = var_315_end_0, end_mask = var_315_end_mask_0, x = input_107_cast_fp16)[name = tensor<string, []>("op_315_cast_fp16")];
tensor<int32, [4]> var_316_begin_0 = const()[name = tensor<string, []>("op_316_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_316_end_0 = const()[name = tensor<string, []>("op_316_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_316_end_mask_0 = const()[name = tensor<string, []>("op_316_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_316_cast_fp16 = slice_by_index(begin = var_316_begin_0, end = var_316_end_0, end_mask = var_316_end_mask_0, x = input_107_cast_fp16)[name = tensor<string, []>("op_316_cast_fp16")];
tensor<bool, []> input_109_interleave_0 = const()[name = tensor<string, []>("input_109_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_109_cast_fp16 = concat(axis = var_24, interleave = input_109_interleave_0, values = (var_315_cast_fp16, input_107_cast_fp16, var_316_cast_fp16))[name = tensor<string, []>("input_109_cast_fp16")];
tensor<int32, [8]> input_111_pad_0 = const()[name = tensor<string, []>("input_111_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_111_mode_0 = const()[name = tensor<string, []>("input_111_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_15_to_fp16 = const()[name = tensor<string, []>("const_15_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_111_cast_fp16 = pad(constant_val = const_15_to_fp16, mode = input_111_mode_0, pad = input_111_pad_0, x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39921856)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44640512)))];
tensor<fp16, [1, 512, 64, 128]> input_113_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 128])];
tensor<fp16, [1, 32, 16, 64, 128]> reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_113_cast_fp16)[name = tensor<string, []>("reshape_40_cast_fp16")];
tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = tensor<string, []>("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_30_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_30_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_30_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor<string, []>("sub_20_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor<string, []>("square_10_cast_fp16")];
tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = tensor<string, []>("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_32_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_32_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_32_cast_fp16")];
tensor<fp16, []> add_20_y_0_to_fp16 = const()[name = tensor<string, []>("add_20_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor<string, []>("add_20_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor<string, []>("sqrt_10_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 128]> real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor<string, []>("real_div_10_cast_fp16")];
tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<fp16, [1, 512, 64, 128]> reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor<string, []>("reshape_41_cast_fp16")];
tensor<fp16, [512]> add_21_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_21_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44641600)))];
tensor<fp16, [512]> add_21_beta_0_to_fp16 = const()[name = tensor<string, []>("add_21_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44642688)))];
tensor<fp16, []> add_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 128]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor<string, []>("add_21_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> input_117_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
tensor<int32, [4]> var_333_begin_0 = const()[name = tensor<string, []>("op_333_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_333_end_0 = const()[name = tensor<string, []>("op_333_end_0"), val = tensor<int32, [4]>([1, 512, 64, 128])];
tensor<bool, [4]> var_333_end_mask_0 = const()[name = tensor<string, []>("op_333_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 64, 1]> var_333_cast_fp16 = slice_by_index(begin = var_333_begin_0, end = var_333_end_0, end_mask = var_333_end_mask_0, x = input_117_cast_fp16)[name = tensor<string, []>("op_333_cast_fp16")];
tensor<int32, [4]> var_334_begin_0 = const()[name = tensor<string, []>("op_334_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_334_end_0 = const()[name = tensor<string, []>("op_334_end_0"), val = tensor<int32, [4]>([1, 512, 64, 1])];
tensor<bool, [4]> var_334_end_mask_0 = const()[name = tensor<string, []>("op_334_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 64, 1]> var_334_cast_fp16 = slice_by_index(begin = var_334_begin_0, end = var_334_end_0, end_mask = var_334_end_mask_0, x = input_117_cast_fp16)[name = tensor<string, []>("op_334_cast_fp16")];
tensor<bool, []> input_121_interleave_0 = const()[name = tensor<string, []>("input_121_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 64, 130]> input_121_cast_fp16 = concat(axis = var_24, interleave = input_121_interleave_0, values = (var_333_cast_fp16, input_117_cast_fp16, var_334_cast_fp16))[name = tensor<string, []>("input_121_cast_fp16")];
tensor<int32, [8]> input_123_pad_0 = const()[name = tensor<string, []>("input_123_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_123_mode_0 = const()[name = tensor<string, []>("input_123_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_16_to_fp16 = const()[name = tensor<string, []>("const_16_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 66, 130]> input_123_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_123_mode_0, pad = input_123_pad_0, x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
tensor<string, []> hidden_states_23_pad_type_0 = const()[name = tensor<string, []>("hidden_states_23_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = tensor<string, []>("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = tensor<string, []>("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = tensor<string, []>("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_23_groups_0 = const()[name = tensor<string, []>("hidden_states_23_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44643776)))];
tensor<fp16, [512]> decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49362432)))];
tensor<fp16, [1, 512, 64, 128]> hidden_states_23_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("hidden_states_23_cast_fp16")];
tensor<fp16, [1, 512, 64, 128]> var_344_cast_fp16 = add(x = var_302_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor<string, []>("op_344_cast_fp16")];
tensor<fp32, []> input_125_scale_factor_height_0 = const()[name = tensor<string, []>("input_125_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp32, []> input_125_scale_factor_width_0 = const()[name = tensor<string, []>("input_125_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp16, [1, 512, 128, 256]> input_125_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_344_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
tensor<int32, [4]> var_352_begin_0 = const()[name = tensor<string, []>("op_352_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_352_end_0 = const()[name = tensor<string, []>("op_352_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_352_end_mask_0 = const()[name = tensor<string, []>("op_352_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_352_cast_fp16 = slice_by_index(begin = var_352_begin_0, end = var_352_end_0, end_mask = var_352_end_mask_0, x = input_125_cast_fp16)[name = tensor<string, []>("op_352_cast_fp16")];
tensor<int32, [4]> var_353_begin_0 = const()[name = tensor<string, []>("op_353_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_353_end_0 = const()[name = tensor<string, []>("op_353_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_353_end_mask_0 = const()[name = tensor<string, []>("op_353_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_353_cast_fp16 = slice_by_index(begin = var_353_begin_0, end = var_353_end_0, end_mask = var_353_end_mask_0, x = input_125_cast_fp16)[name = tensor<string, []>("op_353_cast_fp16")];
tensor<bool, []> input_127_interleave_0 = const()[name = tensor<string, []>("input_127_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_127_cast_fp16 = concat(axis = var_24, interleave = input_127_interleave_0, values = (var_352_cast_fp16, input_125_cast_fp16, var_353_cast_fp16))[name = tensor<string, []>("input_127_cast_fp16")];
tensor<int32, [8]> input_129_pad_0 = const()[name = tensor<string, []>("input_129_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_129_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_129_mode_0, pad = input_129_pad_0, x = input_127_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_131_strides_0 = const()[name = tensor<string, []>("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_131_dilations_0 = const()[name = tensor<string, []>("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_131_groups_0 = const()[name = tensor<string, []>("input_131_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49363520)))];
tensor<fp16, [512]> decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54082176)))];
tensor<fp16, [1, 512, 128, 256]> input_131_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_131_cast_fp16)[name = tensor<string, []>("reshape_44_cast_fp16")];
tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_33_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor<string, []>("sub_22_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor<string, []>("square_11_cast_fp16")];
tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_35_cast_fp16")];
tensor<fp16, []> add_22_y_0_to_fp16 = const()[name = tensor<string, []>("add_22_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor<string, []>("add_22_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor<string, []>("sqrt_11_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor<string, []>("real_div_11_cast_fp16")];
tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor<string, []>("reshape_45_cast_fp16")];
tensor<fp16, [512]> add_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_23_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54083264)))];
tensor<fp16, [512]> add_23_beta_0_to_fp16 = const()[name = tensor<string, []>("add_23_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54084352)))];
tensor<fp16, []> add_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor<string, []>("add_23_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_135_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
tensor<int32, [4]> var_381_begin_0 = const()[name = tensor<string, []>("op_381_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_381_end_0 = const()[name = tensor<string, []>("op_381_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_381_end_mask_0 = const()[name = tensor<string, []>("op_381_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_381_cast_fp16 = slice_by_index(begin = var_381_begin_0, end = var_381_end_0, end_mask = var_381_end_mask_0, x = input_135_cast_fp16)[name = tensor<string, []>("op_381_cast_fp16")];
tensor<int32, [4]> var_382_begin_0 = const()[name = tensor<string, []>("op_382_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_382_end_0 = const()[name = tensor<string, []>("op_382_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_382_end_mask_0 = const()[name = tensor<string, []>("op_382_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_382_cast_fp16 = slice_by_index(begin = var_382_begin_0, end = var_382_end_0, end_mask = var_382_end_mask_0, x = input_135_cast_fp16)[name = tensor<string, []>("op_382_cast_fp16")];
tensor<bool, []> input_137_interleave_0 = const()[name = tensor<string, []>("input_137_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_137_cast_fp16 = concat(axis = var_24, interleave = input_137_interleave_0, values = (var_381_cast_fp16, input_135_cast_fp16, var_382_cast_fp16))[name = tensor<string, []>("input_137_cast_fp16")];
tensor<int32, [8]> input_139_pad_0 = const()[name = tensor<string, []>("input_139_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_139_mode_0 = const()[name = tensor<string, []>("input_139_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_18_to_fp16 = const()[name = tensor<string, []>("const_18_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_139_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_139_mode_0, pad = input_139_pad_0, x = input_137_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
tensor<string, []> input_141_pad_type_0 = const()[name = tensor<string, []>("input_141_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_141_strides_0 = const()[name = tensor<string, []>("input_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_141_pad_0 = const()[name = tensor<string, []>("input_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_141_dilations_0 = const()[name = tensor<string, []>("input_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_141_groups_0 = const()[name = tensor<string, []>("input_141_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54085440)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58804096)))];
tensor<fp16, [1, 512, 128, 256]> input_141_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_141_cast_fp16)[name = tensor<string, []>("reshape_48_cast_fp16")];
tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_36_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor<string, []>("sub_24_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor<string, []>("square_12_cast_fp16")];
tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_38_cast_fp16")];
tensor<fp16, []> add_24_y_0_to_fp16 = const()[name = tensor<string, []>("add_24_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor<string, []>("add_24_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor<string, []>("sqrt_12_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor<string, []>("real_div_12_cast_fp16")];
tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor<string, []>("reshape_49_cast_fp16")];
tensor<fp16, [512]> add_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_25_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58805184)))];
tensor<fp16, [512]> add_25_beta_0_to_fp16 = const()[name = tensor<string, []>("add_25_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58806272)))];
tensor<fp16, []> add_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor<string, []>("add_25_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_145_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
tensor<int32, [4]> var_399_begin_0 = const()[name = tensor<string, []>("op_399_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_399_end_0 = const()[name = tensor<string, []>("op_399_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_399_end_mask_0 = const()[name = tensor<string, []>("op_399_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_399_cast_fp16 = slice_by_index(begin = var_399_begin_0, end = var_399_end_0, end_mask = var_399_end_mask_0, x = input_145_cast_fp16)[name = tensor<string, []>("op_399_cast_fp16")];
tensor<int32, [4]> var_400_begin_0 = const()[name = tensor<string, []>("op_400_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_400_end_0 = const()[name = tensor<string, []>("op_400_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_400_end_mask_0 = const()[name = tensor<string, []>("op_400_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_400_cast_fp16 = slice_by_index(begin = var_400_begin_0, end = var_400_end_0, end_mask = var_400_end_mask_0, x = input_145_cast_fp16)[name = tensor<string, []>("op_400_cast_fp16")];
tensor<bool, []> input_149_interleave_0 = const()[name = tensor<string, []>("input_149_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_149_cast_fp16 = concat(axis = var_24, interleave = input_149_interleave_0, values = (var_399_cast_fp16, input_145_cast_fp16, var_400_cast_fp16))[name = tensor<string, []>("input_149_cast_fp16")];
tensor<int32, [8]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_151_mode_0 = const()[name = tensor<string, []>("input_151_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_19_to_fp16 = const()[name = tensor<string, []>("const_19_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_151_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
tensor<string, []> hidden_states_27_pad_type_0 = const()[name = tensor<string, []>("hidden_states_27_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = tensor<string, []>("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = tensor<string, []>("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = tensor<string, []>("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_27_groups_0 = const()[name = tensor<string, []>("hidden_states_27_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58807360)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63526016)))];
tensor<fp16, [1, 512, 128, 256]> hidden_states_27_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("hidden_states_27_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> var_410_cast_fp16 = add(x = input_131_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor<string, []>("op_410_cast_fp16")];
tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_410_cast_fp16)[name = tensor<string, []>("reshape_52_cast_fp16")];
tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_39_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor<string, []>("sub_26_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor<string, []>("square_13_cast_fp16")];
tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_41_cast_fp16")];
tensor<fp16, []> add_26_y_0_to_fp16 = const()[name = tensor<string, []>("add_26_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor<string, []>("add_26_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor<string, []>("sqrt_13_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor<string, []>("real_div_13_cast_fp16")];
tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor<string, []>("reshape_53_cast_fp16")];
tensor<fp16, [512]> add_27_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_27_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63527104)))];
tensor<fp16, [512]> add_27_beta_0_to_fp16 = const()[name = tensor<string, []>("add_27_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63528192)))];
tensor<fp16, []> add_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor<string, []>("add_27_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_157_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
tensor<int32, [4]> var_423_begin_0 = const()[name = tensor<string, []>("op_423_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_423_end_0 = const()[name = tensor<string, []>("op_423_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_423_end_mask_0 = const()[name = tensor<string, []>("op_423_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_423_cast_fp16 = slice_by_index(begin = var_423_begin_0, end = var_423_end_0, end_mask = var_423_end_mask_0, x = input_157_cast_fp16)[name = tensor<string, []>("op_423_cast_fp16")];
tensor<int32, [4]> var_424_begin_0 = const()[name = tensor<string, []>("op_424_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_424_end_0 = const()[name = tensor<string, []>("op_424_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_424_end_mask_0 = const()[name = tensor<string, []>("op_424_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_424_cast_fp16 = slice_by_index(begin = var_424_begin_0, end = var_424_end_0, end_mask = var_424_end_mask_0, x = input_157_cast_fp16)[name = tensor<string, []>("op_424_cast_fp16")];
tensor<bool, []> input_159_interleave_0 = const()[name = tensor<string, []>("input_159_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_159_cast_fp16 = concat(axis = var_24, interleave = input_159_interleave_0, values = (var_423_cast_fp16, input_157_cast_fp16, var_424_cast_fp16))[name = tensor<string, []>("input_159_cast_fp16")];
tensor<int32, [8]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_161_mode_0 = const()[name = tensor<string, []>("input_161_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_20_to_fp16 = const()[name = tensor<string, []>("const_20_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_161_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_161_mode_0, pad = input_161_pad_0, x = input_159_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
tensor<string, []> input_163_pad_type_0 = const()[name = tensor<string, []>("input_163_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_163_strides_0 = const()[name = tensor<string, []>("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_163_pad_0 = const()[name = tensor<string, []>("input_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_163_dilations_0 = const()[name = tensor<string, []>("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_163_groups_0 = const()[name = tensor<string, []>("input_163_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63529280)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68247936)))];
tensor<fp16, [1, 512, 128, 256]> input_163_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_163_cast_fp16)[name = tensor<string, []>("reshape_56_cast_fp16")];
tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = tensor<string, []>("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_42_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_42_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_42_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor<string, []>("sub_28_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor<string, []>("square_14_cast_fp16")];
tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = tensor<string, []>("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_44_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_44_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_44_cast_fp16")];
tensor<fp16, []> add_28_y_0_to_fp16 = const()[name = tensor<string, []>("add_28_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor<string, []>("add_28_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor<string, []>("sqrt_14_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor<string, []>("real_div_14_cast_fp16")];
tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor<string, []>("reshape_57_cast_fp16")];
tensor<fp16, [512]> add_29_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_29_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68249024)))];
tensor<fp16, [512]> add_29_beta_0_to_fp16 = const()[name = tensor<string, []>("add_29_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68250112)))];
tensor<fp16, []> add_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor<string, []>("add_29_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_167_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
tensor<int32, [4]> var_441_begin_0 = const()[name = tensor<string, []>("op_441_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_441_end_0 = const()[name = tensor<string, []>("op_441_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_441_end_mask_0 = const()[name = tensor<string, []>("op_441_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_441_cast_fp16 = slice_by_index(begin = var_441_begin_0, end = var_441_end_0, end_mask = var_441_end_mask_0, x = input_167_cast_fp16)[name = tensor<string, []>("op_441_cast_fp16")];
tensor<int32, [4]> var_442_begin_0 = const()[name = tensor<string, []>("op_442_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_442_end_0 = const()[name = tensor<string, []>("op_442_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_442_end_mask_0 = const()[name = tensor<string, []>("op_442_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_442_cast_fp16 = slice_by_index(begin = var_442_begin_0, end = var_442_end_0, end_mask = var_442_end_mask_0, x = input_167_cast_fp16)[name = tensor<string, []>("op_442_cast_fp16")];
tensor<bool, []> input_171_interleave_0 = const()[name = tensor<string, []>("input_171_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_171_cast_fp16 = concat(axis = var_24, interleave = input_171_interleave_0, values = (var_441_cast_fp16, input_167_cast_fp16, var_442_cast_fp16))[name = tensor<string, []>("input_171_cast_fp16")];
tensor<int32, [8]> input_173_pad_0 = const()[name = tensor<string, []>("input_173_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_173_mode_0 = const()[name = tensor<string, []>("input_173_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_21_to_fp16 = const()[name = tensor<string, []>("const_21_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_173_cast_fp16 = pad(constant_val = const_21_to_fp16, mode = input_173_mode_0, pad = input_173_pad_0, x = input_171_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
tensor<string, []> hidden_states_29_pad_type_0 = const()[name = tensor<string, []>("hidden_states_29_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_29_strides_0 = const()[name = tensor<string, []>("hidden_states_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = tensor<string, []>("hidden_states_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_29_dilations_0 = const()[name = tensor<string, []>("hidden_states_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_29_groups_0 = const()[name = tensor<string, []>("hidden_states_29_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68251200)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72969856)))];
tensor<fp16, [1, 512, 128, 256]> hidden_states_29_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("hidden_states_29_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> var_452_cast_fp16 = add(x = var_410_cast_fp16, y = hidden_states_29_cast_fp16)[name = tensor<string, []>("op_452_cast_fp16")];
tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_452_cast_fp16)[name = tensor<string, []>("reshape_60_cast_fp16")];
tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_45_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor<string, []>("sub_30_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor<string, []>("square_15_cast_fp16")];
tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_47_cast_fp16")];
tensor<fp16, []> add_30_y_0_to_fp16 = const()[name = tensor<string, []>("add_30_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor<string, []>("add_30_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor<string, []>("sqrt_15_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor<string, []>("real_div_15_cast_fp16")];
tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor<string, []>("reshape_61_cast_fp16")];
tensor<fp16, [512]> add_31_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_31_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72970944)))];
tensor<fp16, [512]> add_31_beta_0_to_fp16 = const()[name = tensor<string, []>("add_31_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72972032)))];
tensor<fp16, []> add_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor<string, []>("add_31_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_179_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
tensor<int32, [4]> var_465_begin_0 = const()[name = tensor<string, []>("op_465_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_465_end_0 = const()[name = tensor<string, []>("op_465_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_465_end_mask_0 = const()[name = tensor<string, []>("op_465_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_465_cast_fp16 = slice_by_index(begin = var_465_begin_0, end = var_465_end_0, end_mask = var_465_end_mask_0, x = input_179_cast_fp16)[name = tensor<string, []>("op_465_cast_fp16")];
tensor<int32, [4]> var_466_begin_0 = const()[name = tensor<string, []>("op_466_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_466_end_0 = const()[name = tensor<string, []>("op_466_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_466_end_mask_0 = const()[name = tensor<string, []>("op_466_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_466_cast_fp16 = slice_by_index(begin = var_466_begin_0, end = var_466_end_0, end_mask = var_466_end_mask_0, x = input_179_cast_fp16)[name = tensor<string, []>("op_466_cast_fp16")];
tensor<bool, []> input_181_interleave_0 = const()[name = tensor<string, []>("input_181_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_181_cast_fp16 = concat(axis = var_24, interleave = input_181_interleave_0, values = (var_465_cast_fp16, input_179_cast_fp16, var_466_cast_fp16))[name = tensor<string, []>("input_181_cast_fp16")];
tensor<int32, [8]> input_183_pad_0 = const()[name = tensor<string, []>("input_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_183_mode_0 = const()[name = tensor<string, []>("input_183_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_22_to_fp16 = const()[name = tensor<string, []>("const_22_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_183_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_183_mode_0, pad = input_183_pad_0, x = input_181_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")];
tensor<string, []> input_185_pad_type_0 = const()[name = tensor<string, []>("input_185_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_185_strides_0 = const()[name = tensor<string, []>("input_185_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_185_pad_0 = const()[name = tensor<string, []>("input_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_185_dilations_0 = const()[name = tensor<string, []>("input_185_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_185_groups_0 = const()[name = tensor<string, []>("input_185_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72973120)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77691776)))];
tensor<fp16, [1, 512, 128, 256]> input_185_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_183_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 256])];
tensor<fp16, [1, 32, 16, 128, 256]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_185_cast_fp16)[name = tensor<string, []>("reshape_64_cast_fp16")];
tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = tensor<string, []>("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_48_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_48_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_48_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor<string, []>("sub_32_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor<string, []>("square_16_cast_fp16")];
tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = tensor<string, []>("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_50_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_50_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_50_cast_fp16")];
tensor<fp16, []> add_32_y_0_to_fp16 = const()[name = tensor<string, []>("add_32_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor<string, []>("add_32_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor<string, []>("sqrt_16_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 256]> real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor<string, []>("real_div_16_cast_fp16")];
tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<fp16, [1, 512, 128, 256]> reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor<string, []>("reshape_65_cast_fp16")];
tensor<fp16, [512]> add_33_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_33_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77692864)))];
tensor<fp16, [512]> add_33_beta_0_to_fp16 = const()[name = tensor<string, []>("add_33_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77693952)))];
tensor<fp16, []> add_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 256]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor<string, []>("add_33_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> input_189_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
tensor<int32, [4]> var_483_begin_0 = const()[name = tensor<string, []>("op_483_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_483_end_0 = const()[name = tensor<string, []>("op_483_end_0"), val = tensor<int32, [4]>([1, 512, 128, 256])];
tensor<bool, [4]> var_483_end_mask_0 = const()[name = tensor<string, []>("op_483_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 128, 1]> var_483_cast_fp16 = slice_by_index(begin = var_483_begin_0, end = var_483_end_0, end_mask = var_483_end_mask_0, x = input_189_cast_fp16)[name = tensor<string, []>("op_483_cast_fp16")];
tensor<int32, [4]> var_484_begin_0 = const()[name = tensor<string, []>("op_484_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_484_end_0 = const()[name = tensor<string, []>("op_484_end_0"), val = tensor<int32, [4]>([1, 512, 128, 1])];
tensor<bool, [4]> var_484_end_mask_0 = const()[name = tensor<string, []>("op_484_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 128, 1]> var_484_cast_fp16 = slice_by_index(begin = var_484_begin_0, end = var_484_end_0, end_mask = var_484_end_mask_0, x = input_189_cast_fp16)[name = tensor<string, []>("op_484_cast_fp16")];
tensor<bool, []> input_193_interleave_0 = const()[name = tensor<string, []>("input_193_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 128, 258]> input_193_cast_fp16 = concat(axis = var_24, interleave = input_193_interleave_0, values = (var_483_cast_fp16, input_189_cast_fp16, var_484_cast_fp16))[name = tensor<string, []>("input_193_cast_fp16")];
tensor<int32, [8]> input_195_pad_0 = const()[name = tensor<string, []>("input_195_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_195_mode_0 = const()[name = tensor<string, []>("input_195_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 130, 258]> input_195_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_195_mode_0, pad = input_195_pad_0, x = input_193_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
tensor<string, []> hidden_states_31_pad_type_0 = const()[name = tensor<string, []>("hidden_states_31_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_31_strides_0 = const()[name = tensor<string, []>("hidden_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = tensor<string, []>("hidden_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_31_dilations_0 = const()[name = tensor<string, []>("hidden_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_31_groups_0 = const()[name = tensor<string, []>("hidden_states_31_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77695040)))];
tensor<fp16, [512]> decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82413696)))];
tensor<fp16, [1, 512, 128, 256]> hidden_states_31_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_31_dilations_0, groups = hidden_states_31_groups_0, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = hidden_states_31_strides_0, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("hidden_states_31_cast_fp16")];
tensor<fp16, [1, 512, 128, 256]> var_494_cast_fp16 = add(x = var_452_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor<string, []>("op_494_cast_fp16")];
tensor<fp32, []> input_197_scale_factor_height_0 = const()[name = tensor<string, []>("input_197_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp32, []> input_197_scale_factor_width_0 = const()[name = tensor<string, []>("input_197_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp16, [1, 512, 256, 512]> input_197_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_197_scale_factor_height_0, scale_factor_width = input_197_scale_factor_width_0, x = var_494_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
tensor<int32, [4]> var_502_begin_0 = const()[name = tensor<string, []>("op_502_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_502_end_0 = const()[name = tensor<string, []>("op_502_end_0"), val = tensor<int32, [4]>([1, 512, 256, 512])];
tensor<bool, [4]> var_502_end_mask_0 = const()[name = tensor<string, []>("op_502_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 256, 1]> var_502_cast_fp16 = slice_by_index(begin = var_502_begin_0, end = var_502_end_0, end_mask = var_502_end_mask_0, x = input_197_cast_fp16)[name = tensor<string, []>("op_502_cast_fp16")];
tensor<int32, [4]> var_503_begin_0 = const()[name = tensor<string, []>("op_503_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_503_end_0 = const()[name = tensor<string, []>("op_503_end_0"), val = tensor<int32, [4]>([1, 512, 256, 1])];
tensor<bool, [4]> var_503_end_mask_0 = const()[name = tensor<string, []>("op_503_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 256, 1]> var_503_cast_fp16 = slice_by_index(begin = var_503_begin_0, end = var_503_end_0, end_mask = var_503_end_mask_0, x = input_197_cast_fp16)[name = tensor<string, []>("op_503_cast_fp16")];
tensor<bool, []> input_199_interleave_0 = const()[name = tensor<string, []>("input_199_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 256, 514]> input_199_cast_fp16 = concat(axis = var_24, interleave = input_199_interleave_0, values = (var_502_cast_fp16, input_197_cast_fp16, var_503_cast_fp16))[name = tensor<string, []>("input_199_cast_fp16")];
tensor<int32, [8]> input_201_pad_0 = const()[name = tensor<string, []>("input_201_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_201_mode_0 = const()[name = tensor<string, []>("input_201_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_24_to_fp16 = const()[name = tensor<string, []>("const_24_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 258, 514]> input_201_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = tensor<string, []>("input_201_cast_fp16")];
tensor<string, []> input_203_pad_type_0 = const()[name = tensor<string, []>("input_203_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_203_strides_0 = const()[name = tensor<string, []>("input_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_203_pad_0 = const()[name = tensor<string, []>("input_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_203_dilations_0 = const()[name = tensor<string, []>("input_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_203_groups_0 = const()[name = tensor<string, []>("input_203_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82414784)))];
tensor<fp16, [512]> decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87133440)))];
tensor<fp16, [1, 512, 256, 512]> input_203_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_201_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 512])];
tensor<fp16, [1, 32, 16, 256, 512]> reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_203_cast_fp16)[name = tensor<string, []>("reshape_68_cast_fp16")];
tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_51_cast_fp16")];
tensor<fp16, [1, 32, 16, 256, 512]> sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor<string, []>("sub_34_cast_fp16")];
tensor<fp16, [1, 32, 16, 256, 512]> square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor<string, []>("square_17_cast_fp16")];
tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_53_cast_fp16")];
tensor<fp16, []> add_34_y_0_to_fp16 = const()[name = tensor<string, []>("add_34_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor<string, []>("add_34_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor<string, []>("sqrt_17_cast_fp16")];
tensor<fp16, [1, 32, 16, 256, 512]> real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor<string, []>("real_div_17_cast_fp16")];
tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 512])];
tensor<fp16, [1, 512, 256, 512]> reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor<string, []>("reshape_69_cast_fp16")];
tensor<fp16, [512]> add_35_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_35_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87134528)))];
tensor<fp16, [512]> add_35_beta_0_to_fp16 = const()[name = tensor<string, []>("add_35_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87135616)))];
tensor<fp16, []> add_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 256, 512]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor<string, []>("add_35_cast_fp16")];
tensor<fp16, [1, 512, 256, 512]> input_207_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
tensor<int32, [4]> var_532_begin_0 = const()[name = tensor<string, []>("op_532_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_532_end_0 = const()[name = tensor<string, []>("op_532_end_0"), val = tensor<int32, [4]>([1, 512, 256, 512])];
tensor<bool, [4]> var_532_end_mask_0 = const()[name = tensor<string, []>("op_532_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 512, 256, 1]> var_532_cast_fp16 = slice_by_index(begin = var_532_begin_0, end = var_532_end_0, end_mask = var_532_end_mask_0, x = input_207_cast_fp16)[name = tensor<string, []>("op_532_cast_fp16")];
tensor<int32, [4]> var_533_begin_0 = const()[name = tensor<string, []>("op_533_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_533_end_0 = const()[name = tensor<string, []>("op_533_end_0"), val = tensor<int32, [4]>([1, 512, 256, 1])];
tensor<bool, [4]> var_533_end_mask_0 = const()[name = tensor<string, []>("op_533_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 512, 256, 1]> var_533_cast_fp16 = slice_by_index(begin = var_533_begin_0, end = var_533_end_0, end_mask = var_533_end_mask_0, x = input_207_cast_fp16)[name = tensor<string, []>("op_533_cast_fp16")];
tensor<bool, []> input_209_interleave_0 = const()[name = tensor<string, []>("input_209_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 256, 514]> input_209_cast_fp16 = concat(axis = var_24, interleave = input_209_interleave_0, values = (var_532_cast_fp16, input_207_cast_fp16, var_533_cast_fp16))[name = tensor<string, []>("input_209_cast_fp16")];
tensor<int32, [8]> input_211_pad_0 = const()[name = tensor<string, []>("input_211_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_211_mode_0 = const()[name = tensor<string, []>("input_211_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_25_to_fp16 = const()[name = tensor<string, []>("const_25_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 258, 514]> input_211_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = tensor<string, []>("input_211_cast_fp16")];
tensor<string, []> input_213_pad_type_0 = const()[name = tensor<string, []>("input_213_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_213_strides_0 = const()[name = tensor<string, []>("input_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_213_pad_0 = const()[name = tensor<string, []>("input_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_213_dilations_0 = const()[name = tensor<string, []>("input_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_213_groups_0 = const()[name = tensor<string, []>("input_213_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 512, 3, 3]> decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87136704)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89496064)))];
tensor<fp16, [1, 256, 256, 512]> input_213_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_211_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")];
tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 512])];
tensor<fp16, [1, 32, 8, 256, 512]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_213_cast_fp16)[name = tensor<string, []>("reshape_72_cast_fp16")];
tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_54_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor<string, []>("sub_36_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor<string, []>("square_18_cast_fp16")];
tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_56_cast_fp16")];
tensor<fp16, []> add_36_y_0_to_fp16 = const()[name = tensor<string, []>("add_36_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor<string, []>("add_36_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor<string, []>("sqrt_18_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor<string, []>("real_div_18_cast_fp16")];
tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<fp16, [1, 256, 256, 512]> reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor<string, []>("reshape_73_cast_fp16")];
tensor<fp16, [256]> add_37_mean_0_to_fp16 = const()[name = tensor<string, []>("add_37_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89496640)))];
tensor<fp16, [256]> add_37_variance_0_to_fp16 = const()[name = tensor<string, []>("add_37_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89497216)))];
tensor<fp16, [256]> add_37_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_37_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89497792)))];
tensor<fp16, [256]> add_37_beta_0_to_fp16 = const()[name = tensor<string, []>("add_37_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89498368)))];
tensor<fp16, []> add_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 512]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor<string, []>("add_37_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> input_217_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")];
tensor<int32, [4]> var_550_begin_0 = const()[name = tensor<string, []>("op_550_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_550_end_0 = const()[name = tensor<string, []>("op_550_end_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<bool, [4]> var_550_end_mask_0 = const()[name = tensor<string, []>("op_550_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 256, 1]> var_550_cast_fp16 = slice_by_index(begin = var_550_begin_0, end = var_550_end_0, end_mask = var_550_end_mask_0, x = input_217_cast_fp16)[name = tensor<string, []>("op_550_cast_fp16")];
tensor<int32, [4]> var_551_begin_0 = const()[name = tensor<string, []>("op_551_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_551_end_0 = const()[name = tensor<string, []>("op_551_end_0"), val = tensor<int32, [4]>([1, 256, 256, 1])];
tensor<bool, [4]> var_551_end_mask_0 = const()[name = tensor<string, []>("op_551_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 256, 1]> var_551_cast_fp16 = slice_by_index(begin = var_551_begin_0, end = var_551_end_0, end_mask = var_551_end_mask_0, x = input_217_cast_fp16)[name = tensor<string, []>("op_551_cast_fp16")];
tensor<bool, []> input_221_interleave_0 = const()[name = tensor<string, []>("input_221_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 256, 514]> input_221_cast_fp16 = concat(axis = var_24, interleave = input_221_interleave_0, values = (var_550_cast_fp16, input_217_cast_fp16, var_551_cast_fp16))[name = tensor<string, []>("input_221_cast_fp16")];
tensor<int32, [8]> input_223_pad_0 = const()[name = tensor<string, []>("input_223_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_223_mode_0 = const()[name = tensor<string, []>("input_223_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_26_to_fp16 = const()[name = tensor<string, []>("const_26_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 258, 514]> input_223_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_223_mode_0, pad = input_223_pad_0, x = input_221_cast_fp16)[name = tensor<string, []>("input_223_cast_fp16")];
tensor<string, []> hidden_states_35_pad_type_0 = const()[name = tensor<string, []>("hidden_states_35_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_35_strides_0 = const()[name = tensor<string, []>("hidden_states_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = tensor<string, []>("hidden_states_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_35_dilations_0 = const()[name = tensor<string, []>("hidden_states_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_35_groups_0 = const()[name = tensor<string, []>("hidden_states_35_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89498944)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90678656)))];
tensor<fp16, [1, 256, 256, 512]> hidden_states_35_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_223_cast_fp16)[name = tensor<string, []>("hidden_states_35_cast_fp16")];
tensor<string, []> input_tensor_1_pad_type_0 = const()[name = tensor<string, []>("input_tensor_1_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_tensor_1_strides_0 = const()[name = tensor<string, []>("input_tensor_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = tensor<string, []>("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_tensor_1_dilations_0 = const()[name = tensor<string, []>("input_tensor_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_tensor_1_groups_0 = const()[name = tensor<string, []>("input_tensor_1_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 512, 1, 1]> decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [256, 512, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90679232)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90941440)))];
tensor<fp16, [1, 256, 256, 512]> input_tensor_1_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_1_dilations_0, groups = input_tensor_1_groups_0, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = input_tensor_1_strides_0, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("input_tensor_1_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> var_568_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor<string, []>("op_568_cast_fp16")];
tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 512])];
tensor<fp16, [1, 32, 8, 256, 512]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_568_cast_fp16)[name = tensor<string, []>("reshape_76_cast_fp16")];
tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_57_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor<string, []>("sub_38_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor<string, []>("square_19_cast_fp16")];
tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_59_cast_fp16")];
tensor<fp16, []> add_38_y_0_to_fp16 = const()[name = tensor<string, []>("add_38_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor<string, []>("add_38_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor<string, []>("sqrt_19_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor<string, []>("real_div_19_cast_fp16")];
tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<fp16, [1, 256, 256, 512]> reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor<string, []>("reshape_77_cast_fp16")];
tensor<fp16, [256]> add_39_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_39_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90942016)))];
tensor<fp16, [256]> add_39_beta_0_to_fp16 = const()[name = tensor<string, []>("add_39_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90942592)))];
tensor<fp16, []> add_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 512]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor<string, []>("add_39_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> input_229_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
tensor<int32, [4]> var_581_begin_0 = const()[name = tensor<string, []>("op_581_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_581_end_0 = const()[name = tensor<string, []>("op_581_end_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<bool, [4]> var_581_end_mask_0 = const()[name = tensor<string, []>("op_581_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 256, 1]> var_581_cast_fp16 = slice_by_index(begin = var_581_begin_0, end = var_581_end_0, end_mask = var_581_end_mask_0, x = input_229_cast_fp16)[name = tensor<string, []>("op_581_cast_fp16")];
tensor<int32, [4]> var_582_begin_0 = const()[name = tensor<string, []>("op_582_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_582_end_0 = const()[name = tensor<string, []>("op_582_end_0"), val = tensor<int32, [4]>([1, 256, 256, 1])];
tensor<bool, [4]> var_582_end_mask_0 = const()[name = tensor<string, []>("op_582_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 256, 1]> var_582_cast_fp16 = slice_by_index(begin = var_582_begin_0, end = var_582_end_0, end_mask = var_582_end_mask_0, x = input_229_cast_fp16)[name = tensor<string, []>("op_582_cast_fp16")];
tensor<bool, []> input_231_interleave_0 = const()[name = tensor<string, []>("input_231_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 256, 514]> input_231_cast_fp16 = concat(axis = var_24, interleave = input_231_interleave_0, values = (var_581_cast_fp16, input_229_cast_fp16, var_582_cast_fp16))[name = tensor<string, []>("input_231_cast_fp16")];
tensor<int32, [8]> input_233_pad_0 = const()[name = tensor<string, []>("input_233_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_233_mode_0 = const()[name = tensor<string, []>("input_233_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_27_to_fp16 = const()[name = tensor<string, []>("const_27_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 258, 514]> input_233_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_233_mode_0, pad = input_233_pad_0, x = input_231_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
tensor<string, []> input_235_pad_type_0 = const()[name = tensor<string, []>("input_235_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_235_strides_0 = const()[name = tensor<string, []>("input_235_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_235_pad_0 = const()[name = tensor<string, []>("input_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_235_dilations_0 = const()[name = tensor<string, []>("input_235_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_235_groups_0 = const()[name = tensor<string, []>("input_235_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90943168)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92122880)))];
tensor<fp16, [1, 256, 256, 512]> input_235_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = input_235_dilations_0, groups = input_235_groups_0, pad = input_235_pad_0, pad_type = input_235_pad_type_0, strides = input_235_strides_0, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_233_cast_fp16)[name = tensor<string, []>("input_235_cast_fp16")];
tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 512])];
tensor<fp16, [1, 32, 8, 256, 512]> reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_235_cast_fp16)[name = tensor<string, []>("reshape_80_cast_fp16")];
tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = tensor<string, []>("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_60_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_60_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_60_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor<string, []>("sub_40_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor<string, []>("square_20_cast_fp16")];
tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = tensor<string, []>("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_62_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_62_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_62_cast_fp16")];
tensor<fp16, []> add_40_y_0_to_fp16 = const()[name = tensor<string, []>("add_40_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor<string, []>("add_40_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor<string, []>("sqrt_20_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor<string, []>("real_div_20_cast_fp16")];
tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<fp16, [1, 256, 256, 512]> reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor<string, []>("reshape_81_cast_fp16")];
tensor<fp16, [256]> add_41_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_41_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92123456)))];
tensor<fp16, [256]> add_41_beta_0_to_fp16 = const()[name = tensor<string, []>("add_41_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92124032)))];
tensor<fp16, []> add_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 512]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor<string, []>("add_41_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> input_239_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor<string, []>("input_239_cast_fp16")];
tensor<int32, [4]> var_599_begin_0 = const()[name = tensor<string, []>("op_599_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_599_end_0 = const()[name = tensor<string, []>("op_599_end_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<bool, [4]> var_599_end_mask_0 = const()[name = tensor<string, []>("op_599_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 256, 1]> var_599_cast_fp16 = slice_by_index(begin = var_599_begin_0, end = var_599_end_0, end_mask = var_599_end_mask_0, x = input_239_cast_fp16)[name = tensor<string, []>("op_599_cast_fp16")];
tensor<int32, [4]> var_600_begin_0 = const()[name = tensor<string, []>("op_600_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_600_end_0 = const()[name = tensor<string, []>("op_600_end_0"), val = tensor<int32, [4]>([1, 256, 256, 1])];
tensor<bool, [4]> var_600_end_mask_0 = const()[name = tensor<string, []>("op_600_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 256, 1]> var_600_cast_fp16 = slice_by_index(begin = var_600_begin_0, end = var_600_end_0, end_mask = var_600_end_mask_0, x = input_239_cast_fp16)[name = tensor<string, []>("op_600_cast_fp16")];
tensor<bool, []> input_243_interleave_0 = const()[name = tensor<string, []>("input_243_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 256, 514]> input_243_cast_fp16 = concat(axis = var_24, interleave = input_243_interleave_0, values = (var_599_cast_fp16, input_239_cast_fp16, var_600_cast_fp16))[name = tensor<string, []>("input_243_cast_fp16")];
tensor<int32, [8]> input_245_pad_0 = const()[name = tensor<string, []>("input_245_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_245_mode_0 = const()[name = tensor<string, []>("input_245_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_28_to_fp16 = const()[name = tensor<string, []>("const_28_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 258, 514]> input_245_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_245_mode_0, pad = input_245_pad_0, x = input_243_cast_fp16)[name = tensor<string, []>("input_245_cast_fp16")];
tensor<string, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = tensor<string, []>("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = tensor<string, []>("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_37_groups_0 = const()[name = tensor<string, []>("hidden_states_37_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92124608)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93304320)))];
tensor<fp16, [1, 256, 256, 512]> hidden_states_37_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_245_cast_fp16)[name = tensor<string, []>("hidden_states_37_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> var_610_cast_fp16 = add(x = var_568_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor<string, []>("op_610_cast_fp16")];
tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 512])];
tensor<fp16, [1, 32, 8, 256, 512]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_610_cast_fp16)[name = tensor<string, []>("reshape_84_cast_fp16")];
tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_63_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor<string, []>("sub_42_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor<string, []>("square_21_cast_fp16")];
tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_65_cast_fp16")];
tensor<fp16, []> add_42_y_0_to_fp16 = const()[name = tensor<string, []>("add_42_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor<string, []>("add_42_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor<string, []>("sqrt_21_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor<string, []>("real_div_21_cast_fp16")];
tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<fp16, [1, 256, 256, 512]> reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor<string, []>("reshape_85_cast_fp16")];
tensor<fp16, [256]> add_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_43_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93304896)))];
tensor<fp16, [256]> add_43_beta_0_to_fp16 = const()[name = tensor<string, []>("add_43_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93305472)))];
tensor<fp16, []> add_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 512]> add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor<string, []>("add_43_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> input_251_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor<string, []>("input_251_cast_fp16")];
tensor<int32, [4]> var_623_begin_0 = const()[name = tensor<string, []>("op_623_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_623_end_0 = const()[name = tensor<string, []>("op_623_end_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<bool, [4]> var_623_end_mask_0 = const()[name = tensor<string, []>("op_623_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 256, 1]> var_623_cast_fp16 = slice_by_index(begin = var_623_begin_0, end = var_623_end_0, end_mask = var_623_end_mask_0, x = input_251_cast_fp16)[name = tensor<string, []>("op_623_cast_fp16")];
tensor<int32, [4]> var_624_begin_0 = const()[name = tensor<string, []>("op_624_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_624_end_0 = const()[name = tensor<string, []>("op_624_end_0"), val = tensor<int32, [4]>([1, 256, 256, 1])];
tensor<bool, [4]> var_624_end_mask_0 = const()[name = tensor<string, []>("op_624_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 256, 1]> var_624_cast_fp16 = slice_by_index(begin = var_624_begin_0, end = var_624_end_0, end_mask = var_624_end_mask_0, x = input_251_cast_fp16)[name = tensor<string, []>("op_624_cast_fp16")];
tensor<bool, []> input_253_interleave_0 = const()[name = tensor<string, []>("input_253_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 256, 514]> input_253_cast_fp16 = concat(axis = var_24, interleave = input_253_interleave_0, values = (var_623_cast_fp16, input_251_cast_fp16, var_624_cast_fp16))[name = tensor<string, []>("input_253_cast_fp16")];
tensor<int32, [8]> input_255_pad_0 = const()[name = tensor<string, []>("input_255_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_255_mode_0 = const()[name = tensor<string, []>("input_255_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_29_to_fp16 = const()[name = tensor<string, []>("const_29_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 258, 514]> input_255_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor<string, []>("input_255_cast_fp16")];
tensor<string, []> input_257_pad_type_0 = const()[name = tensor<string, []>("input_257_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_257_strides_0 = const()[name = tensor<string, []>("input_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_257_pad_0 = const()[name = tensor<string, []>("input_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_257_dilations_0 = const()[name = tensor<string, []>("input_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_257_groups_0 = const()[name = tensor<string, []>("input_257_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93306048)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94485760)))];
tensor<fp16, [1, 256, 256, 512]> input_257_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_255_cast_fp16)[name = tensor<string, []>("input_257_cast_fp16")];
tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 512])];
tensor<fp16, [1, 32, 8, 256, 512]> reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_257_cast_fp16)[name = tensor<string, []>("reshape_88_cast_fp16")];
tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = tensor<string, []>("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_66_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_66_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_66_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor<string, []>("sub_44_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor<string, []>("square_22_cast_fp16")];
tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = tensor<string, []>("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_68_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_68_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_68_cast_fp16")];
tensor<fp16, []> add_44_y_0_to_fp16 = const()[name = tensor<string, []>("add_44_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor<string, []>("add_44_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor<string, []>("sqrt_22_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 512]> real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor<string, []>("real_div_22_cast_fp16")];
tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<fp16, [1, 256, 256, 512]> reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor<string, []>("reshape_89_cast_fp16")];
tensor<fp16, [256]> add_45_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_45_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94486336)))];
tensor<fp16, [256]> add_45_beta_0_to_fp16 = const()[name = tensor<string, []>("add_45_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94486912)))];
tensor<fp16, []> add_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 512]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor<string, []>("add_45_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> input_261_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")];
tensor<int32, [4]> var_641_begin_0 = const()[name = tensor<string, []>("op_641_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_641_end_0 = const()[name = tensor<string, []>("op_641_end_0"), val = tensor<int32, [4]>([1, 256, 256, 512])];
tensor<bool, [4]> var_641_end_mask_0 = const()[name = tensor<string, []>("op_641_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 256, 1]> var_641_cast_fp16 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, x = input_261_cast_fp16)[name = tensor<string, []>("op_641_cast_fp16")];
tensor<int32, [4]> var_642_begin_0 = const()[name = tensor<string, []>("op_642_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_642_end_0 = const()[name = tensor<string, []>("op_642_end_0"), val = tensor<int32, [4]>([1, 256, 256, 1])];
tensor<bool, [4]> var_642_end_mask_0 = const()[name = tensor<string, []>("op_642_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 256, 1]> var_642_cast_fp16 = slice_by_index(begin = var_642_begin_0, end = var_642_end_0, end_mask = var_642_end_mask_0, x = input_261_cast_fp16)[name = tensor<string, []>("op_642_cast_fp16")];
tensor<bool, []> input_265_interleave_0 = const()[name = tensor<string, []>("input_265_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 256, 514]> input_265_cast_fp16 = concat(axis = var_24, interleave = input_265_interleave_0, values = (var_641_cast_fp16, input_261_cast_fp16, var_642_cast_fp16))[name = tensor<string, []>("input_265_cast_fp16")];
tensor<int32, [8]> input_267_pad_0 = const()[name = tensor<string, []>("input_267_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_267_mode_0 = const()[name = tensor<string, []>("input_267_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_30_to_fp16 = const()[name = tensor<string, []>("const_30_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 258, 514]> input_267_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_267_mode_0, pad = input_267_pad_0, x = input_265_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")];
tensor<string, []> hidden_states_39_pad_type_0 = const()[name = tensor<string, []>("hidden_states_39_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_39_strides_0 = const()[name = tensor<string, []>("hidden_states_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = tensor<string, []>("hidden_states_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_39_dilations_0 = const()[name = tensor<string, []>("hidden_states_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_39_groups_0 = const()[name = tensor<string, []>("hidden_states_39_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94487488)))];
tensor<fp16, [256]> decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95667200)))];
tensor<fp16, [1, 256, 256, 512]> hidden_states_39_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_39_dilations_0, groups = hidden_states_39_groups_0, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = hidden_states_39_strides_0, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_267_cast_fp16)[name = tensor<string, []>("hidden_states_39_cast_fp16")];
tensor<fp16, [1, 256, 256, 512]> var_652_cast_fp16 = add(x = var_610_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor<string, []>("op_652_cast_fp16")];
tensor<fp32, []> input_269_scale_factor_height_0 = const()[name = tensor<string, []>("input_269_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp32, []> input_269_scale_factor_width_0 = const()[name = tensor<string, []>("input_269_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
tensor<fp16, [1, 256, 512, 1024]> input_269_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_269_scale_factor_height_0, scale_factor_width = input_269_scale_factor_width_0, x = var_652_cast_fp16)[name = tensor<string, []>("input_269_cast_fp16")];
tensor<int32, [4]> var_660_begin_0 = const()[name = tensor<string, []>("op_660_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_660_end_0 = const()[name = tensor<string, []>("op_660_end_0"), val = tensor<int32, [4]>([1, 256, 512, 1024])];
tensor<bool, [4]> var_660_end_mask_0 = const()[name = tensor<string, []>("op_660_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 512, 1]> var_660_cast_fp16 = slice_by_index(begin = var_660_begin_0, end = var_660_end_0, end_mask = var_660_end_mask_0, x = input_269_cast_fp16)[name = tensor<string, []>("op_660_cast_fp16")];
tensor<int32, [4]> var_661_begin_0 = const()[name = tensor<string, []>("op_661_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_661_end_0 = const()[name = tensor<string, []>("op_661_end_0"), val = tensor<int32, [4]>([1, 256, 512, 1])];
tensor<bool, [4]> var_661_end_mask_0 = const()[name = tensor<string, []>("op_661_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 512, 1]> var_661_cast_fp16 = slice_by_index(begin = var_661_begin_0, end = var_661_end_0, end_mask = var_661_end_mask_0, x = input_269_cast_fp16)[name = tensor<string, []>("op_661_cast_fp16")];
tensor<bool, []> input_271_interleave_0 = const()[name = tensor<string, []>("input_271_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 512, 1026]> input_271_cast_fp16 = concat(axis = var_24, interleave = input_271_interleave_0, values = (var_660_cast_fp16, input_269_cast_fp16, var_661_cast_fp16))[name = tensor<string, []>("input_271_cast_fp16")];
tensor<int32, [8]> input_273_pad_0 = const()[name = tensor<string, []>("input_273_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_273_mode_0 = const()[name = tensor<string, []>("input_273_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_31_to_fp16 = const()[name = tensor<string, []>("const_31_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 514, 1026]> input_273_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_273_mode_0, pad = input_273_pad_0, x = input_271_cast_fp16)[name = tensor<string, []>("input_273_cast_fp16")];
tensor<string, []> input_275_pad_type_0 = const()[name = tensor<string, []>("input_275_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_275_strides_0 = const()[name = tensor<string, []>("input_275_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_275_pad_0 = const()[name = tensor<string, []>("input_275_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_275_dilations_0 = const()[name = tensor<string, []>("input_275_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_275_groups_0 = const()[name = tensor<string, []>("input_275_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95667776)))];
tensor<fp16, [256]> decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96847488)))];
tensor<fp16, [1, 256, 512, 1024]> input_275_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = input_275_dilations_0, groups = input_275_groups_0, pad = input_275_pad_0, pad_type = input_275_pad_type_0, strides = input_275_strides_0, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_273_cast_fp16)[name = tensor<string, []>("input_275_cast_fp16")];
tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 1024])];
tensor<fp16, [1, 32, 8, 512, 1024]> reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_275_cast_fp16)[name = tensor<string, []>("reshape_92_cast_fp16")];
tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = tensor<string, []>("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_69_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_69_cast_fp16")];
tensor<fp16, [1, 32, 8, 512, 1024]> sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor<string, []>("sub_46_cast_fp16")];
tensor<fp16, [1, 32, 8, 512, 1024]> square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor<string, []>("square_23_cast_fp16")];
tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = tensor<string, []>("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_71_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_71_cast_fp16")];
tensor<fp16, []> add_46_y_0_to_fp16 = const()[name = tensor<string, []>("add_46_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor<string, []>("add_46_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor<string, []>("sqrt_23_cast_fp16")];
tensor<fp16, [1, 32, 8, 512, 1024]> real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor<string, []>("real_div_23_cast_fp16")];
tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 1024])];
tensor<fp16, [1, 256, 512, 1024]> reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor<string, []>("reshape_93_cast_fp16")];
tensor<fp16, [256]> add_47_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_47_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96848064)))];
tensor<fp16, [256]> add_47_beta_0_to_fp16 = const()[name = tensor<string, []>("add_47_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96848640)))];
tensor<fp16, []> add_47_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_47_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 512, 1024]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor<string, []>("add_47_cast_fp16")];
tensor<fp16, [1, 256, 512, 1024]> input_279_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor<string, []>("input_279_cast_fp16")];
tensor<int32, [4]> var_688_begin_0 = const()[name = tensor<string, []>("op_688_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_688_end_0 = const()[name = tensor<string, []>("op_688_end_0"), val = tensor<int32, [4]>([1, 256, 512, 1024])];
tensor<bool, [4]> var_688_end_mask_0 = const()[name = tensor<string, []>("op_688_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 256, 512, 1]> var_688_cast_fp16 = slice_by_index(begin = var_688_begin_0, end = var_688_end_0, end_mask = var_688_end_mask_0, x = input_279_cast_fp16)[name = tensor<string, []>("op_688_cast_fp16")];
tensor<int32, [4]> var_689_begin_0 = const()[name = tensor<string, []>("op_689_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_689_end_0 = const()[name = tensor<string, []>("op_689_end_0"), val = tensor<int32, [4]>([1, 256, 512, 1])];
tensor<bool, [4]> var_689_end_mask_0 = const()[name = tensor<string, []>("op_689_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 256, 512, 1]> var_689_cast_fp16 = slice_by_index(begin = var_689_begin_0, end = var_689_end_0, end_mask = var_689_end_mask_0, x = input_279_cast_fp16)[name = tensor<string, []>("op_689_cast_fp16")];
tensor<bool, []> input_281_interleave_0 = const()[name = tensor<string, []>("input_281_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 256, 512, 1026]> input_281_cast_fp16 = concat(axis = var_24, interleave = input_281_interleave_0, values = (var_688_cast_fp16, input_279_cast_fp16, var_689_cast_fp16))[name = tensor<string, []>("input_281_cast_fp16")];
tensor<int32, [8]> input_283_pad_0 = const()[name = tensor<string, []>("input_283_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_283_mode_0 = const()[name = tensor<string, []>("input_283_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_32_to_fp16 = const()[name = tensor<string, []>("const_32_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 514, 1026]> input_283_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_283_mode_0, pad = input_283_pad_0, x = input_281_cast_fp16)[name = tensor<string, []>("input_283_cast_fp16")];
tensor<string, []> input_285_pad_type_0 = const()[name = tensor<string, []>("input_285_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_285_strides_0 = const()[name = tensor<string, []>("input_285_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_285_pad_0 = const()[name = tensor<string, []>("input_285_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_285_dilations_0 = const()[name = tensor<string, []>("input_285_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_285_groups_0 = const()[name = tensor<string, []>("input_285_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 256, 3, 3]> decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [128, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96849216)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97439104)))];
tensor<fp16, [1, 128, 512, 1024]> input_285_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_283_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_285_cast_fp16)[name = tensor<string, []>("reshape_96_cast_fp16")];
tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = tensor<string, []>("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_72_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_72_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_72_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor<string, []>("sub_48_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor<string, []>("square_24_cast_fp16")];
tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = tensor<string, []>("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_74_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_74_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_74_cast_fp16")];
tensor<fp16, []> add_48_y_0_to_fp16 = const()[name = tensor<string, []>("add_48_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor<string, []>("add_48_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor<string, []>("sqrt_24_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor<string, []>("real_div_24_cast_fp16")];
tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor<string, []>("reshape_97_cast_fp16")];
tensor<fp16, [128]> add_49_mean_0_to_fp16 = const()[name = tensor<string, []>("add_49_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97439424)))];
tensor<fp16, [128]> add_49_variance_0_to_fp16 = const()[name = tensor<string, []>("add_49_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97439744)))];
tensor<fp16, [128]> add_49_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_49_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97440064)))];
tensor<fp16, [128]> add_49_beta_0_to_fp16 = const()[name = tensor<string, []>("add_49_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97440384)))];
tensor<fp16, []> add_49_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_49_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor<string, []>("add_49_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_289_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor<string, []>("input_289_cast_fp16")];
tensor<int32, [4]> var_706_begin_0 = const()[name = tensor<string, []>("op_706_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_706_end_0 = const()[name = tensor<string, []>("op_706_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_706_end_mask_0 = const()[name = tensor<string, []>("op_706_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_706_cast_fp16 = slice_by_index(begin = var_706_begin_0, end = var_706_end_0, end_mask = var_706_end_mask_0, x = input_289_cast_fp16)[name = tensor<string, []>("op_706_cast_fp16")];
tensor<int32, [4]> var_707_begin_0 = const()[name = tensor<string, []>("op_707_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_707_end_0 = const()[name = tensor<string, []>("op_707_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_707_end_mask_0 = const()[name = tensor<string, []>("op_707_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_707_cast_fp16 = slice_by_index(begin = var_707_begin_0, end = var_707_end_0, end_mask = var_707_end_mask_0, x = input_289_cast_fp16)[name = tensor<string, []>("op_707_cast_fp16")];
tensor<bool, []> input_293_interleave_0 = const()[name = tensor<string, []>("input_293_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_293_cast_fp16 = concat(axis = var_24, interleave = input_293_interleave_0, values = (var_706_cast_fp16, input_289_cast_fp16, var_707_cast_fp16))[name = tensor<string, []>("input_293_cast_fp16")];
tensor<int32, [8]> input_295_pad_0 = const()[name = tensor<string, []>("input_295_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_295_mode_0 = const()[name = tensor<string, []>("input_295_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_33_to_fp16 = const()[name = tensor<string, []>("const_33_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_295_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")];
tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = tensor<string, []>("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = tensor<string, []>("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_43_groups_0 = const()[name = tensor<string, []>("hidden_states_43_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97440704)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97735680)))];
tensor<fp16, [1, 128, 512, 1024]> hidden_states_43_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_295_cast_fp16)[name = tensor<string, []>("hidden_states_43_cast_fp16")];
tensor<string, []> input_tensor_pad_type_0 = const()[name = tensor<string, []>("input_tensor_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_tensor_strides_0 = const()[name = tensor<string, []>("input_tensor_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_tensor_pad_0 = const()[name = tensor<string, []>("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_tensor_dilations_0 = const()[name = tensor<string, []>("input_tensor_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_tensor_groups_0 = const()[name = tensor<string, []>("input_tensor_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 256, 1, 1]> decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [128, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97736000)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97801600)))];
tensor<fp16, [1, 128, 512, 1024]> input_tensor_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_dilations_0, groups = input_tensor_groups_0, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = input_tensor_strides_0, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_275_cast_fp16)[name = tensor<string, []>("input_tensor_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> var_724_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor<string, []>("op_724_cast_fp16")];
tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_724_cast_fp16)[name = tensor<string, []>("reshape_100_cast_fp16")];
tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = tensor<string, []>("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_75_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_75_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor<string, []>("sub_50_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor<string, []>("square_25_cast_fp16")];
tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = tensor<string, []>("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_77_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_77_cast_fp16")];
tensor<fp16, []> add_50_y_0_to_fp16 = const()[name = tensor<string, []>("add_50_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor<string, []>("add_50_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor<string, []>("sqrt_25_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor<string, []>("real_div_25_cast_fp16")];
tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor<string, []>("reshape_101_cast_fp16")];
tensor<fp16, [128]> add_51_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_51_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97801920)))];
tensor<fp16, [128]> add_51_beta_0_to_fp16 = const()[name = tensor<string, []>("add_51_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97802240)))];
tensor<fp16, []> add_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor<string, []>("add_51_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_301_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor<string, []>("input_301_cast_fp16")];
tensor<int32, [4]> var_737_begin_0 = const()[name = tensor<string, []>("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_737_end_0 = const()[name = tensor<string, []>("op_737_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_737_end_mask_0 = const()[name = tensor<string, []>("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = input_301_cast_fp16)[name = tensor<string, []>("op_737_cast_fp16")];
tensor<int32, [4]> var_738_begin_0 = const()[name = tensor<string, []>("op_738_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_738_end_0 = const()[name = tensor<string, []>("op_738_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_738_end_mask_0 = const()[name = tensor<string, []>("op_738_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_738_cast_fp16 = slice_by_index(begin = var_738_begin_0, end = var_738_end_0, end_mask = var_738_end_mask_0, x = input_301_cast_fp16)[name = tensor<string, []>("op_738_cast_fp16")];
tensor<bool, []> input_303_interleave_0 = const()[name = tensor<string, []>("input_303_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_303_cast_fp16 = concat(axis = var_24, interleave = input_303_interleave_0, values = (var_737_cast_fp16, input_301_cast_fp16, var_738_cast_fp16))[name = tensor<string, []>("input_303_cast_fp16")];
tensor<int32, [8]> input_305_pad_0 = const()[name = tensor<string, []>("input_305_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_305_mode_0 = const()[name = tensor<string, []>("input_305_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_34_to_fp16 = const()[name = tensor<string, []>("const_34_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_305_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = input_305_mode_0, pad = input_305_pad_0, x = input_303_cast_fp16)[name = tensor<string, []>("input_305_cast_fp16")];
tensor<string, []> input_307_pad_type_0 = const()[name = tensor<string, []>("input_307_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_307_strides_0 = const()[name = tensor<string, []>("input_307_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_307_pad_0 = const()[name = tensor<string, []>("input_307_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_307_dilations_0 = const()[name = tensor<string, []>("input_307_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_307_groups_0 = const()[name = tensor<string, []>("input_307_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97802560)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98097536)))];
tensor<fp16, [1, 128, 512, 1024]> input_307_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = input_307_dilations_0, groups = input_307_groups_0, pad = input_307_pad_0, pad_type = input_307_pad_type_0, strides = input_307_strides_0, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_305_cast_fp16)[name = tensor<string, []>("input_307_cast_fp16")];
tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_307_cast_fp16)[name = tensor<string, []>("reshape_104_cast_fp16")];
tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = tensor<string, []>("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_78_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_78_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_78_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor<string, []>("sub_52_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor<string, []>("square_26_cast_fp16")];
tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = tensor<string, []>("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_80_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_80_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_80_cast_fp16")];
tensor<fp16, []> add_52_y_0_to_fp16 = const()[name = tensor<string, []>("add_52_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor<string, []>("add_52_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor<string, []>("sqrt_26_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor<string, []>("real_div_26_cast_fp16")];
tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor<string, []>("reshape_105_cast_fp16")];
tensor<fp16, [128]> add_53_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_53_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98097856)))];
tensor<fp16, [128]> add_53_beta_0_to_fp16 = const()[name = tensor<string, []>("add_53_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98098176)))];
tensor<fp16, []> add_53_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_53_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor<string, []>("add_53_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_311_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor<string, []>("input_311_cast_fp16")];
tensor<int32, [4]> var_755_begin_0 = const()[name = tensor<string, []>("op_755_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_755_end_0 = const()[name = tensor<string, []>("op_755_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_755_end_mask_0 = const()[name = tensor<string, []>("op_755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_755_cast_fp16 = slice_by_index(begin = var_755_begin_0, end = var_755_end_0, end_mask = var_755_end_mask_0, x = input_311_cast_fp16)[name = tensor<string, []>("op_755_cast_fp16")];
tensor<int32, [4]> var_756_begin_0 = const()[name = tensor<string, []>("op_756_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_756_end_0 = const()[name = tensor<string, []>("op_756_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_756_end_mask_0 = const()[name = tensor<string, []>("op_756_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_756_cast_fp16 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = input_311_cast_fp16)[name = tensor<string, []>("op_756_cast_fp16")];
tensor<bool, []> input_315_interleave_0 = const()[name = tensor<string, []>("input_315_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_315_cast_fp16 = concat(axis = var_24, interleave = input_315_interleave_0, values = (var_755_cast_fp16, input_311_cast_fp16, var_756_cast_fp16))[name = tensor<string, []>("input_315_cast_fp16")];
tensor<int32, [8]> input_317_pad_0 = const()[name = tensor<string, []>("input_317_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_317_mode_0 = const()[name = tensor<string, []>("input_317_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_35_to_fp16 = const()[name = tensor<string, []>("const_35_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_317_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_317_mode_0, pad = input_317_pad_0, x = input_315_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")];
tensor<string, []> hidden_states_45_pad_type_0 = const()[name = tensor<string, []>("hidden_states_45_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_45_strides_0 = const()[name = tensor<string, []>("hidden_states_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = tensor<string, []>("hidden_states_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_45_dilations_0 = const()[name = tensor<string, []>("hidden_states_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_45_groups_0 = const()[name = tensor<string, []>("hidden_states_45_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98098496)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98393472)))];
tensor<fp16, [1, 128, 512, 1024]> hidden_states_45_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_45_dilations_0, groups = hidden_states_45_groups_0, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = hidden_states_45_strides_0, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor<string, []>("hidden_states_45_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> var_766_cast_fp16 = add(x = var_724_cast_fp16, y = hidden_states_45_cast_fp16)[name = tensor<string, []>("op_766_cast_fp16")];
tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_766_cast_fp16)[name = tensor<string, []>("reshape_108_cast_fp16")];
tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = tensor<string, []>("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_81_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_81_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor<string, []>("sub_54_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor<string, []>("square_27_cast_fp16")];
tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = tensor<string, []>("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_83_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_83_cast_fp16")];
tensor<fp16, []> add_54_y_0_to_fp16 = const()[name = tensor<string, []>("add_54_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor<string, []>("add_54_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor<string, []>("sqrt_27_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor<string, []>("real_div_27_cast_fp16")];
tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor<string, []>("reshape_109_cast_fp16")];
tensor<fp16, [128]> add_55_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_55_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98393792)))];
tensor<fp16, [128]> add_55_beta_0_to_fp16 = const()[name = tensor<string, []>("add_55_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98394112)))];
tensor<fp16, []> add_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor<string, []>("add_55_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_323_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor<string, []>("input_323_cast_fp16")];
tensor<int32, [4]> var_779_begin_0 = const()[name = tensor<string, []>("op_779_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_779_end_0 = const()[name = tensor<string, []>("op_779_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_779_end_mask_0 = const()[name = tensor<string, []>("op_779_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_779_cast_fp16 = slice_by_index(begin = var_779_begin_0, end = var_779_end_0, end_mask = var_779_end_mask_0, x = input_323_cast_fp16)[name = tensor<string, []>("op_779_cast_fp16")];
tensor<int32, [4]> var_780_begin_0 = const()[name = tensor<string, []>("op_780_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_780_end_0 = const()[name = tensor<string, []>("op_780_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_780_end_mask_0 = const()[name = tensor<string, []>("op_780_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_780_cast_fp16 = slice_by_index(begin = var_780_begin_0, end = var_780_end_0, end_mask = var_780_end_mask_0, x = input_323_cast_fp16)[name = tensor<string, []>("op_780_cast_fp16")];
tensor<bool, []> input_325_interleave_0 = const()[name = tensor<string, []>("input_325_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_325_cast_fp16 = concat(axis = var_24, interleave = input_325_interleave_0, values = (var_779_cast_fp16, input_323_cast_fp16, var_780_cast_fp16))[name = tensor<string, []>("input_325_cast_fp16")];
tensor<int32, [8]> input_327_pad_0 = const()[name = tensor<string, []>("input_327_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_327_mode_0 = const()[name = tensor<string, []>("input_327_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_36_to_fp16 = const()[name = tensor<string, []>("const_36_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_327_cast_fp16 = pad(constant_val = const_36_to_fp16, mode = input_327_mode_0, pad = input_327_pad_0, x = input_325_cast_fp16)[name = tensor<string, []>("input_327_cast_fp16")];
tensor<string, []> input_329_pad_type_0 = const()[name = tensor<string, []>("input_329_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> input_329_strides_0 = const()[name = tensor<string, []>("input_329_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_329_pad_0 = const()[name = tensor<string, []>("input_329_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_329_dilations_0 = const()[name = tensor<string, []>("input_329_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> input_329_groups_0 = const()[name = tensor<string, []>("input_329_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98394432)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98689408)))];
tensor<fp16, [1, 128, 512, 1024]> input_329_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = input_329_dilations_0, groups = input_329_groups_0, pad = input_329_pad_0, pad_type = input_329_pad_type_0, strides = input_329_strides_0, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_327_cast_fp16)[name = tensor<string, []>("input_329_cast_fp16")];
tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_329_cast_fp16)[name = tensor<string, []>("reshape_112_cast_fp16")];
tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = tensor<string, []>("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_84_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_84_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_84_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor<string, []>("sub_56_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor<string, []>("square_28_cast_fp16")];
tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = tensor<string, []>("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_86_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_86_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_86_cast_fp16")];
tensor<fp16, []> add_56_y_0_to_fp16 = const()[name = tensor<string, []>("add_56_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor<string, []>("add_56_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor<string, []>("sqrt_28_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor<string, []>("real_div_28_cast_fp16")];
tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor<string, []>("reshape_113_cast_fp16")];
tensor<fp16, [128]> add_57_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_57_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98689728)))];
tensor<fp16, [128]> add_57_beta_0_to_fp16 = const()[name = tensor<string, []>("add_57_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98690048)))];
tensor<fp16, []> add_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor<string, []>("add_57_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_333_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")];
tensor<int32, [4]> var_797_begin_0 = const()[name = tensor<string, []>("op_797_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_797_end_0 = const()[name = tensor<string, []>("op_797_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_797_end_mask_0 = const()[name = tensor<string, []>("op_797_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_797_cast_fp16 = slice_by_index(begin = var_797_begin_0, end = var_797_end_0, end_mask = var_797_end_mask_0, x = input_333_cast_fp16)[name = tensor<string, []>("op_797_cast_fp16")];
tensor<int32, [4]> var_798_begin_0 = const()[name = tensor<string, []>("op_798_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_798_end_0 = const()[name = tensor<string, []>("op_798_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_798_end_mask_0 = const()[name = tensor<string, []>("op_798_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_798_cast_fp16 = slice_by_index(begin = var_798_begin_0, end = var_798_end_0, end_mask = var_798_end_mask_0, x = input_333_cast_fp16)[name = tensor<string, []>("op_798_cast_fp16")];
tensor<bool, []> input_337_interleave_0 = const()[name = tensor<string, []>("input_337_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_337_cast_fp16 = concat(axis = var_24, interleave = input_337_interleave_0, values = (var_797_cast_fp16, input_333_cast_fp16, var_798_cast_fp16))[name = tensor<string, []>("input_337_cast_fp16")];
tensor<int32, [8]> input_339_pad_0 = const()[name = tensor<string, []>("input_339_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_339_mode_0 = const()[name = tensor<string, []>("input_339_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_37_to_fp16 = const()[name = tensor<string, []>("const_37_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_339_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_339_mode_0, pad = input_339_pad_0, x = input_337_cast_fp16)[name = tensor<string, []>("input_339_cast_fp16")];
tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> hidden_states_strides_0 = const()[name = tensor<string, []>("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> hidden_states_dilations_0 = const()[name = tensor<string, []>("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> hidden_states_groups_0 = const()[name = tensor<string, []>("hidden_states_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98690368)))];
tensor<fp16, [128]> decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98985344)))];
tensor<fp16, [1, 128, 512, 1024]> hidden_states_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_339_cast_fp16)[name = tensor<string, []>("hidden_states_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> var_808_cast_fp16 = add(x = var_766_cast_fp16, y = hidden_states_cast_fp16)[name = tensor<string, []>("op_808_cast_fp16")];
tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 1024])];
tensor<fp16, [1, 32, 4, 512, 1024]> reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_808_cast_fp16)[name = tensor<string, []>("reshape_116_cast_fp16")];
tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = tensor<string, []>("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_87_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_87_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor<string, []>("sub_58_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor<string, []>("square_29_cast_fp16")];
tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = tensor<string, []>("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_89_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> 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<string, []>("reduce_mean_89_cast_fp16")];
tensor<fp16, []> add_58_y_0_to_fp16 = const()[name = tensor<string, []>("add_58_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor<string, []>("add_58_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor<string, []>("sqrt_29_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 1024]> real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor<string, []>("real_div_29_cast_fp16")];
tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<fp16, [1, 128, 512, 1024]> reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor<string, []>("reshape_117_cast_fp16")];
tensor<fp16, [128]> add_59_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_59_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98985664)))];
tensor<fp16, [128]> add_59_beta_0_to_fp16 = const()[name = tensor<string, []>("add_59_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98985984)))];
tensor<fp16, []> add_59_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_59_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 1024]> 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_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor<string, []>("add_59_cast_fp16")];
tensor<fp16, [1, 128, 512, 1024]> input_345_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor<string, []>("input_345_cast_fp16")];
tensor<int32, [4]> var_817_begin_0 = const()[name = tensor<string, []>("op_817_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
tensor<int32, [4]> var_817_end_0 = const()[name = tensor<string, []>("op_817_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1024])];
tensor<bool, [4]> var_817_end_mask_0 = const()[name = tensor<string, []>("op_817_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 128, 512, 1]> var_817_cast_fp16 = slice_by_index(begin = var_817_begin_0, end = var_817_end_0, end_mask = var_817_end_mask_0, x = input_345_cast_fp16)[name = tensor<string, []>("op_817_cast_fp16")];
tensor<int32, [4]> var_818_begin_0 = const()[name = tensor<string, []>("op_818_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_818_end_0 = const()[name = tensor<string, []>("op_818_end_0"), val = tensor<int32, [4]>([1, 128, 512, 1])];
tensor<bool, [4]> var_818_end_mask_0 = const()[name = tensor<string, []>("op_818_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp16, [1, 128, 512, 1]> var_818_cast_fp16 = slice_by_index(begin = var_818_begin_0, end = var_818_end_0, end_mask = var_818_end_mask_0, x = input_345_cast_fp16)[name = tensor<string, []>("op_818_cast_fp16")];
tensor<bool, []> input_347_interleave_0 = const()[name = tensor<string, []>("input_347_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 128, 512, 1026]> input_347_cast_fp16 = concat(axis = var_24, interleave = input_347_interleave_0, values = (var_817_cast_fp16, input_345_cast_fp16, var_818_cast_fp16))[name = tensor<string, []>("input_347_cast_fp16")];
tensor<int32, [8]> input_pad_0 = const()[name = tensor<string, []>("input_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 1, 1, 0, 0])];
tensor<string, []> input_mode_0 = const()[name = tensor<string, []>("input_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> const_38_to_fp16 = const()[name = tensor<string, []>("const_38_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 514, 1026]> input_cast_fp16 = pad(constant_val = const_38_to_fp16, mode = input_mode_0, pad = input_pad_0, x = input_347_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
tensor<string, []> var_827_pad_type_0 = const()[name = tensor<string, []>("op_827_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [2]> var_827_strides_0 = const()[name = tensor<string, []>("op_827_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> var_827_pad_0 = const()[name = tensor<string, []>("op_827_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> var_827_dilations_0 = const()[name = tensor<string, []>("op_827_dilations_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, []> var_827_groups_0 = const()[name = tensor<string, []>("op_827_groups_0"), val = tensor<int32, []>(1)];
tensor<fp16, [3, 128, 3, 3]> decoder_conv_out_weight_to_fp16 = const()[name = tensor<string, []>("decoder_conv_out_weight_to_fp16"), val = tensor<fp16, [3, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98986304)))];
tensor<fp16, [3]> decoder_conv_out_bias_to_fp16 = const()[name = tensor<string, []>("decoder_conv_out_bias_to_fp16"), val = tensor<fp16, [3]>([0x1.514p-8, -0x1.c4cp-6, -0x1.67p-5])];
tensor<fp16, [1, 3, 512, 1024]> var_827_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_827_dilations_0, groups = var_827_groups_0, pad = var_827_pad_0, pad_type = var_827_pad_type_0, strides = var_827_strides_0, weight = decoder_conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("op_827_cast_fp16")];
tensor<string, []> var_827_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_827_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 3, 512, 1024]> image = cast(dtype = var_827_cast_fp16_to_fp32_dtype_0, x = var_827_cast_fp16)[name = tensor<string, []>("cast_37")];
} -> (image);
} |