diff --git "a/dreamshaper-xl-lightning-4step-ios-6bits_split_einsum_compiled/VAEDecoder.mlmodelc/model.mil" "b/dreamshaper-xl-lightning-4step-ios-6bits_split_einsum_compiled/VAEDecoder.mlmodelc/model.mil"
deleted file mode 100644--- "a/dreamshaper-xl-lightning-4step-ios-6bits_split_einsum_compiled/VAEDecoder.mlmodelc/model.mil"
+++ /dev/null
@@ -1,999 +0,0 @@
-program(1.0)
-[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3400.43.1"}, {"coremlc-version", "3400.58.2"}, {"coremltools-component-torch", "2.4.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0"}})]
-{
-    func main<ios16>(tensor<fp16, [1, 4, 128, 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.d7cp-5, 0x1.cf4p-3, -0x1.c7p-4, 0x1.adp-3])];
-            tensor<fp16, [1, 4, 128, 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<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_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, 128, 128]> input_3_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = decoder_conv_in_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
-            tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_3_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_7_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
-            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_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, 128, 128]> input_9_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
-            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_9_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_13_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
-            tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_1_strides_0 = const()[name = tensor<string, []>("hidden_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 128, 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_13_cast_fp16)[name = tensor<string, []>("hidden_states_1_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> var_82_cast_fp16 = add(x = input_3_cast_fp16, y = hidden_states_1_cast_fp16)[name = tensor<string, []>("op_82_cast_fp16")];
-            tensor<int32, [4]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 16384])];
-            tensor<fp16, [1, 32, 16, 16384]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_82_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, 16384]> 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, 16384]> 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, 16384]> 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, 16384])];
-            tensor<fp16, [1, 512, 16384]> 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, 16384]> 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, 16384]> 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_19_perm_0 = const()[name = tensor<string, []>("input_19_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, 16384, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = add_5_cast_fp16)[name = tensor<string, []>("transpose_11")];
-            tensor<fp16, [1, 16384, 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_19_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, 16384, 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_19_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, 16384, 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_19_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
-            tensor<int32, [4]> var_123 = const()[name = tensor<string, []>("op_123"), val = tensor<int32, [4]>([1, -1, 1, 512])];
-            tensor<fp16, [1, 16384, 1, 512]> var_124_cast_fp16 = reshape(shape = var_123, x = linear_0_cast_fp16)[name = tensor<string, []>("op_124_cast_fp16")];
-            tensor<int32, [4]> var_126 = const()[name = tensor<string, []>("op_126"), val = tensor<int32, [4]>([1, -1, 1, 512])];
-            tensor<fp16, [1, 16384, 1, 512]> var_127_cast_fp16 = reshape(shape = var_126, x = linear_1_cast_fp16)[name = tensor<string, []>("op_127_cast_fp16")];
-            tensor<int32, [4]> var_129 = const()[name = tensor<string, []>("op_129"), val = tensor<int32, [4]>([1, -1, 1, 512])];
-            tensor<fp16, [1, 16384, 1, 512]> var_130_cast_fp16 = reshape(shape = var_129, x = linear_2_cast_fp16)[name = tensor<string, []>("op_130_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, 16384, 1, 512]> mul_3_cast_fp16 = mul(x = var_124_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, 16384, 512]> transpose_5 = transpose(perm = transpose_5_perm_0, x = var_127_cast_fp16)[name = tensor<string, []>("transpose_8")];
-            tensor<fp16, [1, 1, 16384, 512]> transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_3_cast_fp16)[name = tensor<string, []>("transpose_9")];
-            tensor<fp16, [1, 1, 16384, 16384]> 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, 16384, 16384]> 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, 16384, 512]> value_cast_fp16 = transpose(perm = value_perm_0, x = var_130_cast_fp16)[name = tensor<string, []>("transpose_10")];
-            tensor<fp16, [1, 1, 16384, 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_133_perm_0 = const()[name = tensor<string, []>("op_133_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
-            tensor<int32, [3]> var_137 = const()[name = tensor<string, []>("op_137"), val = tensor<int32, [3]>([1, -1, 512])];
-            tensor<fp16, [1, 16384, 1, 512]> var_133_cast_fp16 = transpose(perm = var_133_perm_0, x = hidden_states_7_cast_fp16)[name = tensor<string, []>("transpose_7")];
-            tensor<fp16, [1, 16384, 512]> hidden_states_9_cast_fp16 = reshape(shape = var_137, x = var_133_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, 16384, 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_144_perm_0 = const()[name = tensor<string, []>("op_144_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
-            tensor<int32, [4]> var_145 = const()[name = tensor<string, []>("op_145"), val = tensor<int32, [4]>([1, 512, 128, 128])];
-            tensor<fp16, [1, 512, 16384]> var_144_cast_fp16 = transpose(perm = var_144_perm_0, x = linear_3_cast_fp16)[name = tensor<string, []>("transpose_6")];
-            tensor<fp16, [1, 512, 128, 128]> hidden_states_13_cast_fp16 = reshape(shape = var_145, x = var_144_cast_fp16)[name = tensor<string, []>("hidden_states_13_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> hidden_states_15_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_82_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, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_29_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
-            tensor<string, []> input_31_pad_type_0 = const()[name = tensor<string, []>("input_31_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_31_strides_0 = const()[name = tensor<string, []>("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_31_dilations_0 = const()[name = tensor<string, []>("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_31_groups_0 = const()[name = tensor<string, []>("input_31_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, 128, 128]> input_31_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
-            tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_31_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_35_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
-            tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 128, 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_35_cast_fp16)[name = tensor<string, []>("hidden_states_17_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> var_177_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = tensor<string, []>("op_177_cast_fp16")];
-            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_177_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_43_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
-            tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_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, 128, 128]> input_45_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
-            tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_45_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_49_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
-            tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = tensor<string, []>("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 128, 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_49_cast_fp16)[name = tensor<string, []>("hidden_states_19_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> var_216_cast_fp16 = add(x = var_177_cast_fp16, y = hidden_states_19_cast_fp16)[name = tensor<string, []>("op_216_cast_fp16")];
-            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_216_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_57_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
-            tensor<string, []> input_59_pad_type_0 = const()[name = tensor<string, []>("input_59_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_59_pad_0 = const()[name = tensor<string, []>("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_59_strides_0 = const()[name = tensor<string, []>("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_59_dilations_0 = const()[name = tensor<string, []>("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_59_groups_0 = const()[name = tensor<string, []>("input_59_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, 128, 128]> input_59_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
-            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_59_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_63_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
-            tensor<string, []> hidden_states_21_pad_type_0 = const()[name = tensor<string, []>("hidden_states_21_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = tensor<string, []>("hidden_states_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_21_strides_0 = const()[name = tensor<string, []>("hidden_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 128, 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_63_cast_fp16)[name = tensor<string, []>("hidden_states_21_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> var_246_cast_fp16 = add(x = var_216_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor<string, []>("op_246_cast_fp16")];
-            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_246_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_71_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
-            tensor<string, []> input_73_pad_type_0 = const()[name = tensor<string, []>("input_73_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_73_pad_0 = const()[name = tensor<string, []>("input_73_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_73_strides_0 = const()[name = tensor<string, []>("input_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_73_dilations_0 = const()[name = tensor<string, []>("input_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_73_groups_0 = const()[name = tensor<string, []>("input_73_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, 128, 128]> input_73_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
-            tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
-            tensor<fp16, [1, 32, 16, 128, 128]> reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_73_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, 128, 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, 128, 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, 128, 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, 128, 128])];
-            tensor<fp16, [1, 512, 128, 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, 128, 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, 128, 128]> input_77_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
-            tensor<string, []> hidden_states_23_pad_type_0 = const()[name = tensor<string, []>("hidden_states_23_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = tensor<string, []>("hidden_states_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 128, 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_77_cast_fp16)[name = tensor<string, []>("hidden_states_23_cast_fp16")];
-            tensor<fp16, [1, 512, 128, 128]> var_276_cast_fp16 = add(x = var_246_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor<string, []>("op_276_cast_fp16")];
-            tensor<fp32, []> input_81_scale_factor_height_0 = const()[name = tensor<string, []>("input_81_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
-            tensor<fp32, []> input_81_scale_factor_width_0 = const()[name = tensor<string, []>("input_81_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
-            tensor<fp16, [1, 512, 256, 256]> input_81_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
-            tensor<string, []> input_83_pad_type_0 = const()[name = tensor<string, []>("input_83_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_83_pad_0 = const()[name = tensor<string, []>("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_83_strides_0 = const()[name = tensor<string, []>("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_83_dilations_0 = const()[name = tensor<string, []>("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_83_groups_0 = const()[name = tensor<string, []>("input_83_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, 256, 256]> input_83_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_81_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
-            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_83_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_87_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
-            tensor<string, []> input_89_pad_type_0 = const()[name = tensor<string, []>("input_89_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_89_pad_0 = const()[name = tensor<string, []>("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_89_strides_0 = const()[name = tensor<string, []>("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_89_dilations_0 = const()[name = tensor<string, []>("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_89_groups_0 = const()[name = tensor<string, []>("input_89_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, 256, 256]> input_89_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
-            tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_89_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_93_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
-            tensor<string, []> hidden_states_27_pad_type_0 = const()[name = tensor<string, []>("hidden_states_27_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = tensor<string, []>("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 256, 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_93_cast_fp16)[name = tensor<string, []>("hidden_states_27_cast_fp16")];
-            tensor<fp16, [1, 512, 256, 256]> var_324_cast_fp16 = add(x = input_83_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor<string, []>("op_324_cast_fp16")];
-            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_324_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_101_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
-            tensor<string, []> input_103_pad_type_0 = const()[name = tensor<string, []>("input_103_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_103_pad_0 = const()[name = tensor<string, []>("input_103_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_103_strides_0 = const()[name = tensor<string, []>("input_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_103_dilations_0 = const()[name = tensor<string, []>("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_103_groups_0 = const()[name = tensor<string, []>("input_103_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, 256, 256]> input_103_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
-            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_103_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_107_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
-            tensor<string, []> hidden_states_29_pad_type_0 = const()[name = tensor<string, []>("hidden_states_29_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = tensor<string, []>("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 256, 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_107_cast_fp16)[name = tensor<string, []>("hidden_states_29_cast_fp16")];
-            tensor<fp16, [1, 512, 256, 256]> var_354_cast_fp16 = add(x = var_324_cast_fp16, y = hidden_states_29_cast_fp16)[name = tensor<string, []>("op_354_cast_fp16")];
-            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_354_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_115_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
-            tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_117_strides_0 = const()[name = tensor<string, []>("input_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_117_dilations_0 = const()[name = tensor<string, []>("input_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_117_groups_0 = const()[name = tensor<string, []>("input_117_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, 256, 256]> input_117_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
-            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
-            tensor<fp16, [1, 32, 16, 256, 256]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_117_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, 256, 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, 256, 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, 256, 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, 256, 256])];
-            tensor<fp16, [1, 512, 256, 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, 256, 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, 256, 256]> input_121_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
-            tensor<string, []> hidden_states_31_pad_type_0 = const()[name = tensor<string, []>("hidden_states_31_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = tensor<string, []>("hidden_states_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 256, 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_121_cast_fp16)[name = tensor<string, []>("hidden_states_31_cast_fp16")];
-            tensor<fp16, [1, 512, 256, 256]> var_384_cast_fp16 = add(x = var_354_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor<string, []>("op_384_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, 512, 512]> 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_384_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
-            tensor<string, []> input_127_pad_type_0 = const()[name = tensor<string, []>("input_127_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_127_pad_0 = const()[name = tensor<string, []>("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_127_strides_0 = const()[name = tensor<string, []>("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_127_dilations_0 = const()[name = tensor<string, []>("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_127_groups_0 = const()[name = tensor<string, []>("input_127_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, 512, 512]> input_127_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_125_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
-            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 512, 512])];
-            tensor<fp16, [1, 32, 16, 512, 512]> reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_127_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 512, 512, 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, 512, 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, 512, 512]> input_131_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
-            tensor<string, []> input_133_pad_type_0 = const()[name = tensor<string, []>("input_133_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_133_pad_0 = const()[name = tensor<string, []>("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_133_strides_0 = const()[name = tensor<string, []>("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_133_dilations_0 = const()[name = tensor<string, []>("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_133_groups_0 = const()[name = tensor<string, []>("input_133_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, 512, 512]> input_133_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_131_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")];
-            tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
-            tensor<fp16, [1, 32, 8, 512, 512]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_133_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 256, 512, 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, 512, 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, 512, 512]> input_137_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
-            tensor<string, []> hidden_states_35_pad_type_0 = const()[name = tensor<string, []>("hidden_states_35_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = tensor<string, []>("hidden_states_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_35_strides_0 = const()[name = tensor<string, []>("hidden_states_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 512, 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_137_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, 512, 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_127_cast_fp16)[name = tensor<string, []>("input_tensor_1_cast_fp16")];
-            tensor<fp16, [1, 256, 512, 512]> var_440_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor<string, []>("op_440_cast_fp16")];
-            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
-            tensor<fp16, [1, 32, 8, 512, 512]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_440_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 256, 512, 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, 512, 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, 512, 512]> input_145_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
-            tensor<string, []> input_147_pad_type_0 = const()[name = tensor<string, []>("input_147_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_147_pad_0 = const()[name = tensor<string, []>("input_147_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_147_strides_0 = const()[name = tensor<string, []>("input_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_147_dilations_0 = const()[name = tensor<string, []>("input_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_147_groups_0 = const()[name = tensor<string, []>("input_147_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, 512, 512]> input_147_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
-            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
-            tensor<fp16, [1, 32, 8, 512, 512]> reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_147_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 256, 512, 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, 512, 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, 512, 512]> input_151_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
-            tensor<string, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = tensor<string, []>("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 512, 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_151_cast_fp16)[name = tensor<string, []>("hidden_states_37_cast_fp16")];
-            tensor<fp16, [1, 256, 512, 512]> var_470_cast_fp16 = add(x = var_440_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor<string, []>("op_470_cast_fp16")];
-            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
-            tensor<fp16, [1, 32, 8, 512, 512]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_470_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 256, 512, 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, 512, 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, 512, 512]> input_159_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
-            tensor<string, []> input_161_pad_type_0 = const()[name = tensor<string, []>("input_161_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_161_strides_0 = const()[name = tensor<string, []>("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_161_dilations_0 = const()[name = tensor<string, []>("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_161_groups_0 = const()[name = tensor<string, []>("input_161_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, 512, 512]> input_161_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_159_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
-            tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
-            tensor<fp16, [1, 32, 8, 512, 512]> reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_161_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, 512, 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, 512, 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, 512, 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, 512, 512])];
-            tensor<fp16, [1, 256, 512, 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, 512, 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, 512, 512]> input_165_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
-            tensor<string, []> hidden_states_39_pad_type_0 = const()[name = tensor<string, []>("hidden_states_39_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = tensor<string, []>("hidden_states_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_39_strides_0 = const()[name = tensor<string, []>("hidden_states_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 512, 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_165_cast_fp16)[name = tensor<string, []>("hidden_states_39_cast_fp16")];
-            tensor<fp16, [1, 256, 512, 512]> var_500_cast_fp16 = add(x = var_470_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor<string, []>("op_500_cast_fp16")];
-            tensor<fp32, []> input_169_scale_factor_height_0 = const()[name = tensor<string, []>("input_169_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
-            tensor<fp32, []> input_169_scale_factor_width_0 = const()[name = tensor<string, []>("input_169_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
-            tensor<fp16, [1, 256, 1024, 1024]> input_169_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")];
-            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_171_strides_0 = const()[name = tensor<string, []>("input_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_171_dilations_0 = const()[name = tensor<string, []>("input_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_171_groups_0 = const()[name = tensor<string, []>("input_171_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, 1024, 1024]> input_171_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = input_171_dilations_0, groups = input_171_groups_0, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = input_171_strides_0, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_169_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")];
-            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 1024, 1024])];
-            tensor<fp16, [1, 32, 8, 1024, 1024]> reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_171_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 256, 1024, 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, 1024, 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, 1024, 1024]> input_175_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
-            tensor<string, []> input_177_pad_type_0 = const()[name = tensor<string, []>("input_177_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_177_pad_0 = const()[name = tensor<string, []>("input_177_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_177_strides_0 = const()[name = tensor<string, []>("input_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_177_dilations_0 = const()[name = tensor<string, []>("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_177_groups_0 = const()[name = tensor<string, []>("input_177_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, 1024, 1024]> input_177_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
-            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_177_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_181_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
-            tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 1024, 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_181_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, 1024, 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_171_cast_fp16)[name = tensor<string, []>("input_tensor_cast_fp16")];
-            tensor<fp16, [1, 128, 1024, 1024]> var_554_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor<string, []>("op_554_cast_fp16")];
-            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_554_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_189_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
-            tensor<string, []> input_191_pad_type_0 = const()[name = tensor<string, []>("input_191_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_191_pad_0 = const()[name = tensor<string, []>("input_191_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_191_strides_0 = const()[name = tensor<string, []>("input_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_191_dilations_0 = const()[name = tensor<string, []>("input_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_191_groups_0 = const()[name = tensor<string, []>("input_191_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, 1024, 1024]> input_191_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = input_191_dilations_0, groups = input_191_groups_0, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = input_191_strides_0, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
-            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_191_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_195_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
-            tensor<string, []> hidden_states_45_pad_type_0 = const()[name = tensor<string, []>("hidden_states_45_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = tensor<string, []>("hidden_states_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            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, [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, 1024, 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_195_cast_fp16)[name = tensor<string, []>("hidden_states_45_cast_fp16")];
-            tensor<fp16, [1, 128, 1024, 1024]> var_584_cast_fp16 = add(x = var_554_cast_fp16, y = hidden_states_45_cast_fp16)[name = tensor<string, []>("op_584_cast_fp16")];
-            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_584_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_203_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
-            tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_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, 1024, 1024]> input_205_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("input_205_cast_fp16")];
-            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_205_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_209_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
-            tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> hidden_states_strides_0 = const()[name = tensor<string, []>("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            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, 1024, 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_209_cast_fp16)[name = tensor<string, []>("hidden_states_cast_fp16")];
-            tensor<fp16, [1, 128, 1024, 1024]> var_614_cast_fp16 = add(x = var_584_cast_fp16, y = hidden_states_cast_fp16)[name = tensor<string, []>("op_614_cast_fp16")];
-            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])];
-            tensor<fp16, [1, 32, 4, 1024, 1024]> reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_614_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, 1024, 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, 1024, 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, 1024, 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, 1024, 1024])];
-            tensor<fp16, [1, 128, 1024, 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, 1024, 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, 1024, 1024]> input_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
-            tensor<string, []> var_627_pad_type_0 = const()[name = tensor<string, []>("op_627_pad_type_0"), val = tensor<string, []>("custom")];
-            tensor<int32, [4]> var_627_pad_0 = const()[name = tensor<string, []>("op_627_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
-            tensor<int32, [2]> var_627_strides_0 = const()[name = tensor<string, []>("op_627_strides_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, [2]> var_627_dilations_0 = const()[name = tensor<string, []>("op_627_dilations_0"), val = tensor<int32, [2]>([1, 1])];
-            tensor<int32, []> var_627_groups_0 = const()[name = tensor<string, []>("op_627_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.f78p-4, 0x1.5p-4, 0x1.a28p-5])];
-            tensor<fp16, [1, 3, 1024, 1024]> var_627_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_627_dilations_0, groups = var_627_groups_0, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_627_strides_0, weight = decoder_conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("op_627_cast_fp16")];
-            tensor<string, []> var_627_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_627_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
-            tensor<fp32, [1, 3, 1024, 1024]> image = cast(dtype = var_627_cast_fp16_to_fp32_dtype_0, x = var_627_cast_fp16)[name = tensor<string, []>("cast_37")];
-        } -> (image);
-}
\ No newline at end of file