program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.2.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.2"}})]
{
    func main<ios16>(tensor<fp32, [1, 4, 64, 64]> z) {
            tensor<fp32, [4]> post_quant_conv_bias = const()[name = tensor<string, []>("post_quant_conv_bias"), val = tensor<fp32, [4]>([0x1.06cp-5, -0x1.594p-4, -0x1.f24p-3, 0x1.0d8p-3])];
            tensor<fp32, [4, 4, 1, 1]> post_quant_conv_weight = const()[name = tensor<string, []>("post_quant_conv_weight"), val = tensor<fp32, [4, 4, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [512]> decoder_conv_in_bias = const()[name = tensor<string, []>("decoder_conv_in_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192)))];
            tensor<fp32, [512, 4, 3, 3]> decoder_conv_in_weight = const()[name = tensor<string, []>("decoder_conv_in_weight"), val = tensor<fp32, [512, 4, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2304)))];
            tensor<fp32, [512]> decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76096)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78208)))];
            tensor<fp32, [512]> decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9515456)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9517568)))];
            tensor<fp32, [512]> decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18954816)))];
            tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18956928)))];
            tensor<fp32, [512]> decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20005568)))];
            tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20007680)))];
            tensor<fp32, [512]> decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21056320)))];
            tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21058432)))];
            tensor<fp32, [512]> decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22107072)))];
            tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22109184)))];
            tensor<fp32, [512]> decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23157824)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23159936)))];
            tensor<fp32, [512]> decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32597184)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32599296)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42036544)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42038656)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51475904)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51478016)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60915264)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60917376)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70354624)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70356736)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79793984)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79796096)))];
            tensor<fp32, [512]> decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89233344)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89235456)))];
            tensor<fp32, [512]> decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98672704)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98674816)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108112064)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108114176)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117551424)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117553536)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126990784)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126992896)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136430144)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136432256)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145869504)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145871616)))];
            tensor<fp32, [512]> decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155308864)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155310976)))];
            tensor<fp32, [512]> decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164748224)))];
            tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164750336)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174187584)))];
            tensor<fp32, [256, 512, 3, 3]> decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor<fp32, [256, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174188672)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178907328)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178908416)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181267776)))];
            tensor<fp32, [256, 512, 1, 1]> decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [256, 512, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181268864)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181793216)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181794304)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184153664)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184154752)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186514112)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186515200)))];
            tensor<fp32, [256]> decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188874560)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188875648)))];
            tensor<fp32, [256]> decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191235008)))];
            tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191236096)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193595456)))];
            tensor<fp32, [128, 256, 3, 3]> decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor<fp32, [128, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193596032)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194775744)))];
            tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194776320)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195366208)))];
            tensor<fp32, [128, 256, 1, 1]> decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [128, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195366784)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195497920)))];
            tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195498496)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196088384)))];
            tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196088960)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196678848)))];
            tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196679424)))];
            tensor<fp32, [128]> decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197269312)))];
            tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197269888)))];
            tensor<fp32, [3]> decoder_conv_out_bias = const()[name = tensor<string, []>("decoder_conv_out_bias"), val = tensor<fp32, [3]>([0x1.02p-6, -0x1.4ccp-6, -0x1.7bcp-5])];
            tensor<fp32, [3, 128, 3, 3]> decoder_conv_out_weight = const()[name = tensor<string, []>("decoder_conv_out_weight"), val = tensor<fp32, [3, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197859776)))];
            tensor<int32, []> var_7 = const()[name = tensor<string, []>("op_7"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12 = const()[name = tensor<string, []>("op_12"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp32, [1, 4, 64, 64]> input_1 = conv(bias = post_quant_conv_bias, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight, x = z)[name = tensor<string, []>("input_1")];
            tensor<int32, []> var_26 = const()[name = tensor<string, []>("op_26"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_44 = const()[name = tensor<string, []>("op_44"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_46 = const()[name = tensor<string, []>("op_46"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 64, 64]> input_3 = conv(bias = decoder_conv_in_bias, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight, x = input_1)[name = tensor<string, []>("input_3")];
            tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_0 = reshape(shape = reshape_0_shape_0, x = input_3)[name = tensor<string, []>("reshape_0")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor<string, []>("reduce_mean_0")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor<string, []>("sub_0")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_0 = square(x = sub_0)[name = tensor<string, []>("square_0")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor<string, []>("reduce_mean_2")];
            tensor<fp32, []> add_0_y_0 = const()[name = tensor<string, []>("add_0_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor<string, []>("add_0")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_0 = sqrt(x = add_0)[name = tensor<string, []>("sqrt_0")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor<string, []>("real_div_0")];
            tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor<string, []>("reshape_1")];
            tensor<fp32, [512]> add_1_mean_0 = const()[name = tensor<string, []>("add_1_mean_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197873664)))];
            tensor<fp32, [512]> add_1_variance_0 = const()[name = tensor<string, []>("add_1_variance_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197875776)))];
            tensor<fp32, [512]> add_1_gamma_0 = const()[name = tensor<string, []>("add_1_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197877888)))];
            tensor<fp32, [512]> add_1_beta_0 = const()[name = tensor<string, []>("add_1_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197880000)))];
            tensor<fp32, []> add_1_epsilon_0 = const()[name = tensor<string, []>("add_1_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor<string, []>("add_1")];
            tensor<fp32, [1, 512, 64, 64]> input_7 = silu(x = add_1)[name = tensor<string, []>("input_7")];
            tensor<int32, [2]> var_65 = const()[name = tensor<string, []>("op_65"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_67 = const()[name = tensor<string, []>("op_67"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> input_9 = conv(bias = decoder_mid_block_resnets_0_conv1_bias, dilations = var_67, groups = var_26, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight, x = input_7)[name = tensor<string, []>("input_9")];
            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_4 = reshape(shape = reshape_4_shape_0, x = input_9)[name = tensor<string, []>("reshape_4")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor<string, []>("reduce_mean_3")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor<string, []>("sub_2")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_1 = square(x = sub_2)[name = tensor<string, []>("square_1")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor<string, []>("reduce_mean_5")];
            tensor<fp32, []> add_2_y_0 = const()[name = tensor<string, []>("add_2_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor<string, []>("add_2")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_1 = sqrt(x = add_2)[name = tensor<string, []>("sqrt_1")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor<string, []>("real_div_1")];
            tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor<string, []>("reshape_5")];
            tensor<fp32, [512]> add_3_gamma_0 = const()[name = tensor<string, []>("add_3_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197882112)))];
            tensor<fp32, [512]> add_3_beta_0 = const()[name = tensor<string, []>("add_3_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197884224)))];
            tensor<fp32, []> add_3_epsilon_0 = const()[name = tensor<string, []>("add_3_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor<string, []>("add_3")];
            tensor<fp32, [1, 512, 64, 64]> input_13 = silu(x = add_3)[name = tensor<string, []>("input_13")];
            tensor<int32, [2]> var_77 = const()[name = tensor<string, []>("op_77"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_79 = const()[name = tensor<string, []>("op_79"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_1 = conv(bias = decoder_mid_block_resnets_0_conv2_bias, dilations = var_79, groups = var_26, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight, x = input_13)[name = tensor<string, []>("hidden_states_1")];
            tensor<fp32, [1, 512, 64, 64]> var_82 = add(x = input_3, y = hidden_states_1)[name = tensor<string, []>("op_82")];
            tensor<int32, [4]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 4096])];
            tensor<fp32, [1, 32, 16, 4096]> reshape_8 = reshape(shape = reshape_8_shape_0, x = var_82)[name = tensor<string, []>("reshape_8")];
            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<fp32, [1, 32, 1, 1]> reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor<string, []>("reduce_mean_6")];
            tensor<fp32, [1, 32, 16, 4096]> sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor<string, []>("sub_4")];
            tensor<fp32, [1, 32, 16, 4096]> square_2 = square(x = sub_4)[name = tensor<string, []>("square_2")];
            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<fp32, [1, 32, 1, 1]> reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor<string, []>("reduce_mean_8")];
            tensor<fp32, []> add_4_y_0 = const()[name = tensor<string, []>("add_4_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1]> add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor<string, []>("add_4")];
            tensor<fp32, [1, 32, 1, 1]> sqrt_2 = sqrt(x = add_4)[name = tensor<string, []>("sqrt_2")];
            tensor<fp32, [1, 32, 16, 4096]> real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor<string, []>("real_div_2")];
            tensor<int32, [3]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 512, 4096])];
            tensor<fp32, [1, 512, 4096]> reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor<string, []>("reshape_9")];
            tensor<fp32, [1, 512, 1]> reshape_10 = const()[name = tensor<string, []>("reshape_10"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197886336)))];
            tensor<fp32, [1, 512, 4096]> mul_2 = mul(x = reshape_9, y = reshape_10)[name = tensor<string, []>("mul_2")];
            tensor<fp32, [1, 512, 1]> reshape_11 = const()[name = tensor<string, []>("reshape_11"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197888448)))];
            tensor<fp32, [1, 512, 4096]> add_5 = add(x = mul_2, y = reshape_11)[name = tensor<string, []>("add_5")];
            tensor<int32, [3]> input_19_perm_0 = const()[name = tensor<string, []>("input_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 4096, 512]> transpose_11 = transpose(perm = input_19_perm_0, x = add_5)[name = tensor<string, []>("transpose_11")];
            tensor<fp32, [1, 4096, 512]> linear_0 = linear(bias = decoder_mid_block_attentions_0_to_q_bias, weight = decoder_mid_block_attentions_0_to_q_weight, x = transpose_11)[name = tensor<string, []>("linear_0")];
            tensor<fp32, [1, 4096, 512]> linear_1 = linear(bias = decoder_mid_block_attentions_0_to_k_bias, weight = decoder_mid_block_attentions_0_to_k_weight, x = transpose_11)[name = tensor<string, []>("linear_1")];
            tensor<fp32, [1, 4096, 512]> linear_2 = linear(bias = decoder_mid_block_attentions_0_to_v_bias, weight = decoder_mid_block_attentions_0_to_v_weight, x = transpose_11)[name = tensor<string, []>("linear_2")];
            tensor<int32, [4]> var_123 = const()[name = tensor<string, []>("op_123"), val = tensor<int32, [4]>([1, -1, 1, 512])];
            tensor<fp32, [1, 4096, 1, 512]> var_124 = reshape(shape = var_123, x = linear_0)[name = tensor<string, []>("op_124")];
            tensor<int32, [4]> var_126 = const()[name = tensor<string, []>("op_126"), val = tensor<int32, [4]>([1, -1, 1, 512])];
            tensor<fp32, [1, 4096, 1, 512]> var_127 = reshape(shape = var_126, x = linear_1)[name = tensor<string, []>("op_127")];
            tensor<int32, [4]> var_129 = const()[name = tensor<string, []>("op_129"), val = tensor<int32, [4]>([1, -1, 1, 512])];
            tensor<fp32, [1, 4096, 1, 512]> var_130 = reshape(shape = var_129, x = linear_2)[name = tensor<string, []>("op_130")];
            tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, []> mul_3_y_0 = const()[name = tensor<string, []>("mul_3_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-5)];
            tensor<fp32, [1, 4096, 1, 512]> mul_3 = mul(x = var_124, y = mul_3_y_0)[name = tensor<string, []>("mul_3")];
            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<fp32, [1, 1, 4096, 512]> transpose_8 = transpose(perm = transpose_5_perm_0, x = var_127)[name = tensor<string, []>("transpose_8")];
            tensor<fp32, [1, 1, 4096, 512]> transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_3)[name = tensor<string, []>("transpose_9")];
            tensor<fp32, [1, 1, 4096, 4096]> matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor<string, []>("matmul_0")];
            tensor<int32, []> softmax_0_axis_0 = const()[name = tensor<string, []>("softmax_0_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp32, [1, 1, 4096, 4096]> softmax_0 = softmax(axis = softmax_0_axis_0, x = matmul_0)[name = tensor<string, []>("softmax_0")];
            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<fp32, [1, 1, 4096, 512]> transpose_10 = transpose(perm = value_perm_0, x = var_130)[name = tensor<string, []>("transpose_10")];
            tensor<fp32, [1, 1, 4096, 512]> hidden_states_7 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0, y = transpose_10)[name = tensor<string, []>("hidden_states_7")];
            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<fp32, [1, 4096, 1, 512]> transpose_7 = transpose(perm = var_133_perm_0, x = hidden_states_7)[name = tensor<string, []>("transpose_7")];
            tensor<fp32, [1, 4096, 512]> hidden_states_9 = reshape(shape = var_137, x = transpose_7)[name = tensor<string, []>("hidden_states_9")];
            tensor<fp32, [1, 4096, 512]> linear_3 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias, weight = decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_9)[name = tensor<string, []>("linear_3")];
            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, 64, 64])];
            tensor<fp32, [1, 512, 4096]> transpose_6 = transpose(perm = var_144_perm_0, x = linear_3)[name = tensor<string, []>("transpose_6")];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_13 = reshape(shape = var_145, x = transpose_6)[name = tensor<string, []>("hidden_states_13")];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_15 = add(x = hidden_states_13, y = var_82)[name = tensor<string, []>("hidden_states_15")];
            tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_15)[name = tensor<string, []>("reshape_12")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor<string, []>("reduce_mean_9")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor<string, []>("sub_6")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_3 = square(x = sub_6)[name = tensor<string, []>("square_3")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor<string, []>("reduce_mean_11")];
            tensor<fp32, []> add_6_y_0 = const()[name = tensor<string, []>("add_6_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor<string, []>("add_6")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_3 = sqrt(x = add_6)[name = tensor<string, []>("sqrt_3")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor<string, []>("real_div_3")];
            tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor<string, []>("reshape_13")];
            tensor<fp32, [512]> add_7_gamma_0 = const()[name = tensor<string, []>("add_7_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197890560)))];
            tensor<fp32, [512]> add_7_beta_0 = const()[name = tensor<string, []>("add_7_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197892672)))];
            tensor<fp32, []> add_7_epsilon_0 = const()[name = tensor<string, []>("add_7_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor<string, []>("add_7")];
            tensor<fp32, [1, 512, 64, 64]> input_29 = silu(x = add_7)[name = tensor<string, []>("input_29")];
            tensor<int32, [2]> var_160 = const()[name = tensor<string, []>("op_160"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_162 = const()[name = tensor<string, []>("op_162"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> input_31 = conv(bias = decoder_mid_block_resnets_1_conv1_bias, dilations = var_162, groups = var_26, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight, x = input_29)[name = tensor<string, []>("input_31")];
            tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_16 = reshape(shape = reshape_16_shape_0, x = input_31)[name = tensor<string, []>("reshape_16")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor<string, []>("reduce_mean_12")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor<string, []>("sub_8")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_4 = square(x = sub_8)[name = tensor<string, []>("square_4")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor<string, []>("reduce_mean_14")];
            tensor<fp32, []> add_8_y_0 = const()[name = tensor<string, []>("add_8_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor<string, []>("add_8")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_4 = sqrt(x = add_8)[name = tensor<string, []>("sqrt_4")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor<string, []>("real_div_4")];
            tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor<string, []>("reshape_17")];
            tensor<fp32, [512]> add_9_gamma_0 = const()[name = tensor<string, []>("add_9_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197894784)))];
            tensor<fp32, [512]> add_9_beta_0 = const()[name = tensor<string, []>("add_9_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197896896)))];
            tensor<fp32, []> add_9_epsilon_0 = const()[name = tensor<string, []>("add_9_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor<string, []>("add_9")];
            tensor<fp32, [1, 512, 64, 64]> input_35 = silu(x = add_9)[name = tensor<string, []>("input_35")];
            tensor<int32, [2]> var_172 = const()[name = tensor<string, []>("op_172"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_174 = const()[name = tensor<string, []>("op_174"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_17 = conv(bias = decoder_mid_block_resnets_1_conv2_bias, dilations = var_174, groups = var_26, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight, x = input_35)[name = tensor<string, []>("hidden_states_17")];
            tensor<fp32, [1, 512, 64, 64]> var_177 = add(x = hidden_states_15, y = hidden_states_17)[name = tensor<string, []>("op_177")];
            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_20 = reshape(shape = reshape_20_shape_0, x = var_177)[name = tensor<string, []>("reshape_20")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor<string, []>("reduce_mean_15")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor<string, []>("sub_10")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_5 = square(x = sub_10)[name = tensor<string, []>("square_5")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor<string, []>("reduce_mean_17")];
            tensor<fp32, []> add_10_y_0 = const()[name = tensor<string, []>("add_10_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor<string, []>("add_10")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_5 = sqrt(x = add_10)[name = tensor<string, []>("sqrt_5")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor<string, []>("real_div_5")];
            tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor<string, []>("reshape_21")];
            tensor<fp32, [512]> add_11_gamma_0 = const()[name = tensor<string, []>("add_11_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197899008)))];
            tensor<fp32, [512]> add_11_beta_0 = const()[name = tensor<string, []>("add_11_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197901120)))];
            tensor<fp32, []> add_11_epsilon_0 = const()[name = tensor<string, []>("add_11_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_21)[name = tensor<string, []>("add_11")];
            tensor<fp32, [1, 512, 64, 64]> input_43 = silu(x = add_11)[name = tensor<string, []>("input_43")];
            tensor<int32, [2]> var_199 = const()[name = tensor<string, []>("op_199"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_201 = const()[name = tensor<string, []>("op_201"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> input_45 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias, dilations = var_201, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight, x = input_43)[name = tensor<string, []>("input_45")];
            tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_24 = reshape(shape = reshape_24_shape_0, x = input_45)[name = tensor<string, []>("reshape_24")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor<string, []>("reduce_mean_18")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor<string, []>("sub_12")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_6 = square(x = sub_12)[name = tensor<string, []>("square_6")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor<string, []>("reduce_mean_20")];
            tensor<fp32, []> add_12_y_0 = const()[name = tensor<string, []>("add_12_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor<string, []>("add_12")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_6 = sqrt(x = add_12)[name = tensor<string, []>("sqrt_6")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor<string, []>("real_div_6")];
            tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor<string, []>("reshape_25")];
            tensor<fp32, [512]> add_13_gamma_0 = const()[name = tensor<string, []>("add_13_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197903232)))];
            tensor<fp32, [512]> add_13_beta_0 = const()[name = tensor<string, []>("add_13_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197905344)))];
            tensor<fp32, []> add_13_epsilon_0 = const()[name = tensor<string, []>("add_13_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_25)[name = tensor<string, []>("add_13")];
            tensor<fp32, [1, 512, 64, 64]> input_49 = silu(x = add_13)[name = tensor<string, []>("input_49")];
            tensor<int32, [2]> var_211 = const()[name = tensor<string, []>("op_211"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_213 = const()[name = tensor<string, []>("op_213"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_19 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias, dilations = var_213, groups = var_26, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight, x = input_49)[name = tensor<string, []>("hidden_states_19")];
            tensor<fp32, [1, 512, 64, 64]> var_216 = add(x = var_177, y = hidden_states_19)[name = tensor<string, []>("op_216")];
            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_28 = reshape(shape = reshape_28_shape_0, x = var_216)[name = tensor<string, []>("reshape_28")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor<string, []>("reduce_mean_21")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor<string, []>("sub_14")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_7 = square(x = sub_14)[name = tensor<string, []>("square_7")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor<string, []>("reduce_mean_23")];
            tensor<fp32, []> add_14_y_0 = const()[name = tensor<string, []>("add_14_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor<string, []>("add_14")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_7 = sqrt(x = add_14)[name = tensor<string, []>("sqrt_7")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor<string, []>("real_div_7")];
            tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor<string, []>("reshape_29")];
            tensor<fp32, [512]> add_15_gamma_0 = const()[name = tensor<string, []>("add_15_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197907456)))];
            tensor<fp32, [512]> add_15_beta_0 = const()[name = tensor<string, []>("add_15_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197909568)))];
            tensor<fp32, []> add_15_epsilon_0 = const()[name = tensor<string, []>("add_15_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_29)[name = tensor<string, []>("add_15")];
            tensor<fp32, [1, 512, 64, 64]> input_57 = silu(x = add_15)[name = tensor<string, []>("input_57")];
            tensor<int32, [2]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> input_59 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias, dilations = var_231, groups = var_26, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight, x = input_57)[name = tensor<string, []>("input_59")];
            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_32 = reshape(shape = reshape_32_shape_0, x = input_59)[name = tensor<string, []>("reshape_32")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor<string, []>("reduce_mean_24")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor<string, []>("sub_16")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_8 = square(x = sub_16)[name = tensor<string, []>("square_8")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor<string, []>("reduce_mean_26")];
            tensor<fp32, []> add_16_y_0 = const()[name = tensor<string, []>("add_16_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor<string, []>("add_16")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_8 = sqrt(x = add_16)[name = tensor<string, []>("sqrt_8")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor<string, []>("real_div_8")];
            tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor<string, []>("reshape_33")];
            tensor<fp32, [512]> add_17_gamma_0 = const()[name = tensor<string, []>("add_17_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197911680)))];
            tensor<fp32, [512]> add_17_beta_0 = const()[name = tensor<string, []>("add_17_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197913792)))];
            tensor<fp32, []> add_17_epsilon_0 = const()[name = tensor<string, []>("add_17_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_33)[name = tensor<string, []>("add_17")];
            tensor<fp32, [1, 512, 64, 64]> input_63 = silu(x = add_17)[name = tensor<string, []>("input_63")];
            tensor<int32, [2]> var_241 = const()[name = tensor<string, []>("op_241"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_243 = const()[name = tensor<string, []>("op_243"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_21_pad_type_0 = const()[name = tensor<string, []>("hidden_states_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = tensor<string, []>("hidden_states_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 64, 64]> hidden_states_21 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias, dilations = var_243, groups = var_26, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight, x = input_63)[name = tensor<string, []>("hidden_states_21")];
            tensor<fp32, [1, 512, 64, 64]> var_246 = add(x = var_216, y = hidden_states_21)[name = tensor<string, []>("op_246")];
            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_36 = reshape(shape = reshape_36_shape_0, x = var_246)[name = tensor<string, []>("reshape_36")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor<string, []>("reduce_mean_27")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor<string, []>("sub_18")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_9 = square(x = sub_18)[name = tensor<string, []>("square_9")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor<string, []>("reduce_mean_29")];
            tensor<fp32, []> add_18_y_0 = const()[name = tensor<string, []>("add_18_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor<string, []>("add_18")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_9 = sqrt(x = add_18)[name = tensor<string, []>("sqrt_9")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor<string, []>("real_div_9")];
            tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor<string, []>("reshape_37")];
            tensor<fp32, [512]> add_19_gamma_0 = const()[name = tensor<string, []>("add_19_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197915904)))];
            tensor<fp32, [512]> add_19_beta_0 = const()[name = tensor<string, []>("add_19_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197918016)))];
            tensor<fp32, []> add_19_epsilon_0 = const()[name = tensor<string, []>("add_19_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_37)[name = tensor<string, []>("add_19")];
            tensor<fp32, [1, 512, 64, 64]> input_71 = silu(x = add_19)[name = tensor<string, []>("input_71")];
            tensor<int32, [2]> var_259 = const()[name = tensor<string, []>("op_259"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> input_73 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias, dilations = var_261, groups = var_26, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight, x = input_71)[name = tensor<string, []>("input_73")];
            tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
            tensor<fp32, [1, 32, 16, 64, 64]> reshape_40 = reshape(shape = reshape_40_shape_0, x = input_73)[name = tensor<string, []>("reshape_40")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor<string, []>("reduce_mean_30")];
            tensor<fp32, [1, 32, 16, 64, 64]> sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor<string, []>("sub_20")];
            tensor<fp32, [1, 32, 16, 64, 64]> square_10 = square(x = sub_20)[name = tensor<string, []>("square_10")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor<string, []>("reduce_mean_32")];
            tensor<fp32, []> add_20_y_0 = const()[name = tensor<string, []>("add_20_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor<string, []>("add_20")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_10 = sqrt(x = add_20)[name = tensor<string, []>("sqrt_10")];
            tensor<fp32, [1, 32, 16, 64, 64]> real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor<string, []>("real_div_10")];
            tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
            tensor<fp32, [1, 512, 64, 64]> reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor<string, []>("reshape_41")];
            tensor<fp32, [512]> add_21_gamma_0 = const()[name = tensor<string, []>("add_21_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197920128)))];
            tensor<fp32, [512]> add_21_beta_0 = const()[name = tensor<string, []>("add_21_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197922240)))];
            tensor<fp32, []> add_21_epsilon_0 = const()[name = tensor<string, []>("add_21_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 64, 64]> add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_41)[name = tensor<string, []>("add_21")];
            tensor<fp32, [1, 512, 64, 64]> input_77 = silu(x = add_21)[name = tensor<string, []>("input_77")];
            tensor<int32, [2]> var_271 = const()[name = tensor<string, []>("op_271"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_273 = const()[name = tensor<string, []>("op_273"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 64, 64]> hidden_states_23 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias, dilations = var_273, groups = var_26, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight, x = input_77)[name = tensor<string, []>("hidden_states_23")];
            tensor<fp32, [1, 512, 64, 64]> var_276 = add(x = var_246, y = hidden_states_23)[name = tensor<string, []>("op_276")];
            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<fp32, [1, 512, 128, 128]> input_81 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276)[name = tensor<string, []>("input_81")];
            tensor<int32, [2]> var_284 = const()[name = tensor<string, []>("op_284"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_286 = const()[name = tensor<string, []>("op_286"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> 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<fp32, [1, 512, 128, 128]> input_83 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = var_286, groups = var_26, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_81)[name = tensor<string, []>("input_83")];
            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_44 = reshape(shape = reshape_44_shape_0, x = input_83)[name = tensor<string, []>("reshape_44")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor<string, []>("reduce_mean_33")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor<string, []>("sub_22")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_11 = square(x = sub_22)[name = tensor<string, []>("square_11")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor<string, []>("reduce_mean_35")];
            tensor<fp32, []> add_22_y_0 = const()[name = tensor<string, []>("add_22_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor<string, []>("add_22")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_11 = sqrt(x = add_22)[name = tensor<string, []>("sqrt_11")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor<string, []>("real_div_11")];
            tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor<string, []>("reshape_45")];
            tensor<fp32, [512]> add_23_gamma_0 = const()[name = tensor<string, []>("add_23_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197924352)))];
            tensor<fp32, [512]> add_23_beta_0 = const()[name = tensor<string, []>("add_23_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197926464)))];
            tensor<fp32, []> add_23_epsilon_0 = const()[name = tensor<string, []>("add_23_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_45)[name = tensor<string, []>("add_23")];
            tensor<fp32, [1, 512, 128, 128]> input_87 = silu(x = add_23)[name = tensor<string, []>("input_87")];
            tensor<int32, [2]> var_307 = const()[name = tensor<string, []>("op_307"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 128, 128]> input_89 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias, dilations = var_309, groups = var_26, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight, x = input_87)[name = tensor<string, []>("input_89")];
            tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_48 = reshape(shape = reshape_48_shape_0, x = input_89)[name = tensor<string, []>("reshape_48")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor<string, []>("reduce_mean_36")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor<string, []>("sub_24")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_12 = square(x = sub_24)[name = tensor<string, []>("square_12")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor<string, []>("reduce_mean_38")];
            tensor<fp32, []> add_24_y_0 = const()[name = tensor<string, []>("add_24_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor<string, []>("add_24")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_12 = sqrt(x = add_24)[name = tensor<string, []>("sqrt_12")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor<string, []>("real_div_12")];
            tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor<string, []>("reshape_49")];
            tensor<fp32, [512]> add_25_gamma_0 = const()[name = tensor<string, []>("add_25_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197928576)))];
            tensor<fp32, [512]> add_25_beta_0 = const()[name = tensor<string, []>("add_25_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197930688)))];
            tensor<fp32, []> add_25_epsilon_0 = const()[name = tensor<string, []>("add_25_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_49)[name = tensor<string, []>("add_25")];
            tensor<fp32, [1, 512, 128, 128]> input_93 = silu(x = add_25)[name = tensor<string, []>("input_93")];
            tensor<int32, [2]> var_319 = const()[name = tensor<string, []>("op_319"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_321 = const()[name = tensor<string, []>("op_321"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 128, 128]> hidden_states_27 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias, dilations = var_321, groups = var_26, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight, x = input_93)[name = tensor<string, []>("hidden_states_27")];
            tensor<fp32, [1, 512, 128, 128]> var_324 = add(x = input_83, y = hidden_states_27)[name = tensor<string, []>("op_324")];
            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_52 = reshape(shape = reshape_52_shape_0, x = var_324)[name = tensor<string, []>("reshape_52")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor<string, []>("reduce_mean_39")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor<string, []>("sub_26")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_13 = square(x = sub_26)[name = tensor<string, []>("square_13")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor<string, []>("reduce_mean_41")];
            tensor<fp32, []> add_26_y_0 = const()[name = tensor<string, []>("add_26_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor<string, []>("add_26")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_13 = sqrt(x = add_26)[name = tensor<string, []>("sqrt_13")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor<string, []>("real_div_13")];
            tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor<string, []>("reshape_53")];
            tensor<fp32, [512]> add_27_gamma_0 = const()[name = tensor<string, []>("add_27_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197932800)))];
            tensor<fp32, [512]> add_27_beta_0 = const()[name = tensor<string, []>("add_27_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197934912)))];
            tensor<fp32, []> add_27_epsilon_0 = const()[name = tensor<string, []>("add_27_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_53)[name = tensor<string, []>("add_27")];
            tensor<fp32, [1, 512, 128, 128]> input_101 = silu(x = add_27)[name = tensor<string, []>("input_101")];
            tensor<int32, [2]> var_337 = const()[name = tensor<string, []>("op_337"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_339 = const()[name = tensor<string, []>("op_339"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 128, 128]> input_103 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias, dilations = var_339, groups = var_26, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight, x = input_101)[name = tensor<string, []>("input_103")];
            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_56 = reshape(shape = reshape_56_shape_0, x = input_103)[name = tensor<string, []>("reshape_56")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor<string, []>("reduce_mean_42")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor<string, []>("sub_28")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_14 = square(x = sub_28)[name = tensor<string, []>("square_14")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor<string, []>("reduce_mean_44")];
            tensor<fp32, []> add_28_y_0 = const()[name = tensor<string, []>("add_28_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor<string, []>("add_28")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_14 = sqrt(x = add_28)[name = tensor<string, []>("sqrt_14")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor<string, []>("real_div_14")];
            tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor<string, []>("reshape_57")];
            tensor<fp32, [512]> add_29_gamma_0 = const()[name = tensor<string, []>("add_29_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197937024)))];
            tensor<fp32, [512]> add_29_beta_0 = const()[name = tensor<string, []>("add_29_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197939136)))];
            tensor<fp32, []> add_29_epsilon_0 = const()[name = tensor<string, []>("add_29_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_57)[name = tensor<string, []>("add_29")];
            tensor<fp32, [1, 512, 128, 128]> input_107 = silu(x = add_29)[name = tensor<string, []>("input_107")];
            tensor<int32, [2]> var_349 = const()[name = tensor<string, []>("op_349"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_351 = const()[name = tensor<string, []>("op_351"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 128, 128]> hidden_states_29 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias, dilations = var_351, groups = var_26, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight, x = input_107)[name = tensor<string, []>("hidden_states_29")];
            tensor<fp32, [1, 512, 128, 128]> var_354 = add(x = var_324, y = hidden_states_29)[name = tensor<string, []>("op_354")];
            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_60 = reshape(shape = reshape_60_shape_0, x = var_354)[name = tensor<string, []>("reshape_60")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor<string, []>("reduce_mean_45")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor<string, []>("sub_30")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_15 = square(x = sub_30)[name = tensor<string, []>("square_15")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor<string, []>("reduce_mean_47")];
            tensor<fp32, []> add_30_y_0 = const()[name = tensor<string, []>("add_30_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor<string, []>("add_30")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_15 = sqrt(x = add_30)[name = tensor<string, []>("sqrt_15")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor<string, []>("real_div_15")];
            tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor<string, []>("reshape_61")];
            tensor<fp32, [512]> add_31_gamma_0 = const()[name = tensor<string, []>("add_31_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197941248)))];
            tensor<fp32, [512]> add_31_beta_0 = const()[name = tensor<string, []>("add_31_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197943360)))];
            tensor<fp32, []> add_31_epsilon_0 = const()[name = tensor<string, []>("add_31_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_61)[name = tensor<string, []>("add_31")];
            tensor<fp32, [1, 512, 128, 128]> input_115 = silu(x = add_31)[name = tensor<string, []>("input_115")];
            tensor<int32, [2]> var_367 = const()[name = tensor<string, []>("op_367"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_369 = const()[name = tensor<string, []>("op_369"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 512, 128, 128]> input_117 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias, dilations = var_369, groups = var_26, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight, x = input_115)[name = tensor<string, []>("input_117")];
            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
            tensor<fp32, [1, 32, 16, 128, 128]> reshape_64 = reshape(shape = reshape_64_shape_0, x = input_117)[name = tensor<string, []>("reshape_64")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor<string, []>("reduce_mean_48")];
            tensor<fp32, [1, 32, 16, 128, 128]> sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor<string, []>("sub_32")];
            tensor<fp32, [1, 32, 16, 128, 128]> square_16 = square(x = sub_32)[name = tensor<string, []>("square_16")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor<string, []>("reduce_mean_50")];
            tensor<fp32, []> add_32_y_0 = const()[name = tensor<string, []>("add_32_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor<string, []>("add_32")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_16 = sqrt(x = add_32)[name = tensor<string, []>("sqrt_16")];
            tensor<fp32, [1, 32, 16, 128, 128]> real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor<string, []>("real_div_16")];
            tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
            tensor<fp32, [1, 512, 128, 128]> reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor<string, []>("reshape_65")];
            tensor<fp32, [512]> add_33_gamma_0 = const()[name = tensor<string, []>("add_33_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197945472)))];
            tensor<fp32, [512]> add_33_beta_0 = const()[name = tensor<string, []>("add_33_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197947584)))];
            tensor<fp32, []> add_33_epsilon_0 = const()[name = tensor<string, []>("add_33_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 128, 128]> add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_65)[name = tensor<string, []>("add_33")];
            tensor<fp32, [1, 512, 128, 128]> input_121 = silu(x = add_33)[name = tensor<string, []>("input_121")];
            tensor<int32, [2]> var_379 = const()[name = tensor<string, []>("op_379"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_381 = const()[name = tensor<string, []>("op_381"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 128, 128]> hidden_states_31 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias, dilations = var_381, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight, x = input_121)[name = tensor<string, []>("hidden_states_31")];
            tensor<fp32, [1, 512, 128, 128]> var_384 = add(x = var_354, y = hidden_states_31)[name = tensor<string, []>("op_384")];
            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<fp32, [1, 512, 256, 256]> input_125 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_384)[name = tensor<string, []>("input_125")];
            tensor<int32, [2]> var_392 = const()[name = tensor<string, []>("op_392"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_394 = const()[name = tensor<string, []>("op_394"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 512, 256, 256]> input_127 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = var_394, groups = var_26, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_125)[name = tensor<string, []>("input_127")];
            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])];
            tensor<fp32, [1, 32, 16, 256, 256]> reshape_68 = reshape(shape = reshape_68_shape_0, x = input_127)[name = tensor<string, []>("reshape_68")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor<string, []>("reduce_mean_51")];
            tensor<fp32, [1, 32, 16, 256, 256]> sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor<string, []>("sub_34")];
            tensor<fp32, [1, 32, 16, 256, 256]> square_17 = square(x = sub_34)[name = tensor<string, []>("square_17")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor<string, []>("reduce_mean_53")];
            tensor<fp32, []> add_34_y_0 = const()[name = tensor<string, []>("add_34_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor<string, []>("add_34")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_17 = sqrt(x = add_34)[name = tensor<string, []>("sqrt_17")];
            tensor<fp32, [1, 32, 16, 256, 256]> real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor<string, []>("real_div_17")];
            tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])];
            tensor<fp32, [1, 512, 256, 256]> reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor<string, []>("reshape_69")];
            tensor<fp32, [512]> add_35_gamma_0 = const()[name = tensor<string, []>("add_35_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197949696)))];
            tensor<fp32, [512]> add_35_beta_0 = const()[name = tensor<string, []>("add_35_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197951808)))];
            tensor<fp32, []> add_35_epsilon_0 = const()[name = tensor<string, []>("add_35_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, 256, 256]> add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_69)[name = tensor<string, []>("add_35")];
            tensor<fp32, [1, 512, 256, 256]> input_131 = silu(x = add_35)[name = tensor<string, []>("input_131")];
            tensor<int32, [2]> var_416 = const()[name = tensor<string, []>("op_416"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_418 = const()[name = tensor<string, []>("op_418"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 256, 256, 256]> input_133 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias, dilations = var_418, groups = var_26, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight, x = input_131)[name = tensor<string, []>("input_133")];
            tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
            tensor<fp32, [1, 32, 8, 256, 256]> reshape_72 = reshape(shape = reshape_72_shape_0, x = input_133)[name = tensor<string, []>("reshape_72")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor<string, []>("reduce_mean_54")];
            tensor<fp32, [1, 32, 8, 256, 256]> sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor<string, []>("sub_36")];
            tensor<fp32, [1, 32, 8, 256, 256]> square_18 = square(x = sub_36)[name = tensor<string, []>("square_18")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor<string, []>("reduce_mean_56")];
            tensor<fp32, []> add_36_y_0 = const()[name = tensor<string, []>("add_36_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor<string, []>("add_36")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_18 = sqrt(x = add_36)[name = tensor<string, []>("sqrt_18")];
            tensor<fp32, [1, 32, 8, 256, 256]> real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor<string, []>("real_div_18")];
            tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
            tensor<fp32, [1, 256, 256, 256]> reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor<string, []>("reshape_73")];
            tensor<fp32, [256]> add_37_mean_0 = const()[name = tensor<string, []>("add_37_mean_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197953920)))];
            tensor<fp32, [256]> add_37_variance_0 = const()[name = tensor<string, []>("add_37_variance_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197955008)))];
            tensor<fp32, [256]> add_37_gamma_0 = const()[name = tensor<string, []>("add_37_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197956096)))];
            tensor<fp32, [256]> add_37_beta_0 = const()[name = tensor<string, []>("add_37_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197957184)))];
            tensor<fp32, []> add_37_epsilon_0 = const()[name = tensor<string, []>("add_37_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 256, 256]> add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_73)[name = tensor<string, []>("add_37")];
            tensor<fp32, [1, 256, 256, 256]> input_137 = silu(x = add_37)[name = tensor<string, []>("input_137")];
            tensor<int32, [2]> var_428 = const()[name = tensor<string, []>("op_428"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_430 = const()[name = tensor<string, []>("op_430"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_35_pad_type_0 = const()[name = tensor<string, []>("hidden_states_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = tensor<string, []>("hidden_states_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 256, 256, 256]> hidden_states_35 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias, dilations = var_430, groups = var_26, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight, x = input_137)[name = tensor<string, []>("hidden_states_35")];
            tensor<int32, [2]> var_435 = const()[name = tensor<string, []>("op_435"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_437 = const()[name = tensor<string, []>("op_437"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_tensor_1_pad_type_0 = const()[name = tensor<string, []>("input_tensor_1_pad_type_0"), val = tensor<string, []>("custom")];
            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<fp32, [1, 256, 256, 256]> input_tensor_1 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_127)[name = tensor<string, []>("input_tensor_1")];
            tensor<fp32, [1, 256, 256, 256]> var_440 = add(x = input_tensor_1, y = hidden_states_35)[name = tensor<string, []>("op_440")];
            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
            tensor<fp32, [1, 32, 8, 256, 256]> reshape_76 = reshape(shape = reshape_76_shape_0, x = var_440)[name = tensor<string, []>("reshape_76")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor<string, []>("reduce_mean_57")];
            tensor<fp32, [1, 32, 8, 256, 256]> sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor<string, []>("sub_38")];
            tensor<fp32, [1, 32, 8, 256, 256]> square_19 = square(x = sub_38)[name = tensor<string, []>("square_19")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor<string, []>("reduce_mean_59")];
            tensor<fp32, []> add_38_y_0 = const()[name = tensor<string, []>("add_38_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor<string, []>("add_38")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_19 = sqrt(x = add_38)[name = tensor<string, []>("sqrt_19")];
            tensor<fp32, [1, 32, 8, 256, 256]> real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor<string, []>("real_div_19")];
            tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
            tensor<fp32, [1, 256, 256, 256]> reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor<string, []>("reshape_77")];
            tensor<fp32, [256]> add_39_gamma_0 = const()[name = tensor<string, []>("add_39_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197958272)))];
            tensor<fp32, [256]> add_39_beta_0 = const()[name = tensor<string, []>("add_39_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197959360)))];
            tensor<fp32, []> add_39_epsilon_0 = const()[name = tensor<string, []>("add_39_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 256, 256]> add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_77)[name = tensor<string, []>("add_39")];
            tensor<fp32, [1, 256, 256, 256]> input_145 = silu(x = add_39)[name = tensor<string, []>("input_145")];
            tensor<int32, [2]> var_453 = const()[name = tensor<string, []>("op_453"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_455 = const()[name = tensor<string, []>("op_455"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 256, 256, 256]> input_147 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias, dilations = var_455, groups = var_26, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight, x = input_145)[name = tensor<string, []>("input_147")];
            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
            tensor<fp32, [1, 32, 8, 256, 256]> reshape_80 = reshape(shape = reshape_80_shape_0, x = input_147)[name = tensor<string, []>("reshape_80")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor<string, []>("reduce_mean_60")];
            tensor<fp32, [1, 32, 8, 256, 256]> sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor<string, []>("sub_40")];
            tensor<fp32, [1, 32, 8, 256, 256]> square_20 = square(x = sub_40)[name = tensor<string, []>("square_20")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor<string, []>("reduce_mean_62")];
            tensor<fp32, []> add_40_y_0 = const()[name = tensor<string, []>("add_40_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor<string, []>("add_40")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_20 = sqrt(x = add_40)[name = tensor<string, []>("sqrt_20")];
            tensor<fp32, [1, 32, 8, 256, 256]> real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor<string, []>("real_div_20")];
            tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
            tensor<fp32, [1, 256, 256, 256]> reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor<string, []>("reshape_81")];
            tensor<fp32, [256]> add_41_gamma_0 = const()[name = tensor<string, []>("add_41_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197960448)))];
            tensor<fp32, [256]> add_41_beta_0 = const()[name = tensor<string, []>("add_41_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197961536)))];
            tensor<fp32, []> add_41_epsilon_0 = const()[name = tensor<string, []>("add_41_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 256, 256]> add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_81)[name = tensor<string, []>("add_41")];
            tensor<fp32, [1, 256, 256, 256]> input_151 = silu(x = add_41)[name = tensor<string, []>("input_151")];
            tensor<int32, [2]> var_465 = const()[name = tensor<string, []>("op_465"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_467 = const()[name = tensor<string, []>("op_467"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 256, 256, 256]> hidden_states_37 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias, dilations = var_467, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight, x = input_151)[name = tensor<string, []>("hidden_states_37")];
            tensor<fp32, [1, 256, 256, 256]> var_470 = add(x = var_440, y = hidden_states_37)[name = tensor<string, []>("op_470")];
            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
            tensor<fp32, [1, 32, 8, 256, 256]> reshape_84 = reshape(shape = reshape_84_shape_0, x = var_470)[name = tensor<string, []>("reshape_84")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor<string, []>("reduce_mean_63")];
            tensor<fp32, [1, 32, 8, 256, 256]> sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor<string, []>("sub_42")];
            tensor<fp32, [1, 32, 8, 256, 256]> square_21 = square(x = sub_42)[name = tensor<string, []>("square_21")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor<string, []>("reduce_mean_65")];
            tensor<fp32, []> add_42_y_0 = const()[name = tensor<string, []>("add_42_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor<string, []>("add_42")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_21 = sqrt(x = add_42)[name = tensor<string, []>("sqrt_21")];
            tensor<fp32, [1, 32, 8, 256, 256]> real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor<string, []>("real_div_21")];
            tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
            tensor<fp32, [1, 256, 256, 256]> reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor<string, []>("reshape_85")];
            tensor<fp32, [256]> add_43_gamma_0 = const()[name = tensor<string, []>("add_43_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197962624)))];
            tensor<fp32, [256]> add_43_beta_0 = const()[name = tensor<string, []>("add_43_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197963712)))];
            tensor<fp32, []> add_43_epsilon_0 = const()[name = tensor<string, []>("add_43_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 256, 256]> add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_85)[name = tensor<string, []>("add_43")];
            tensor<fp32, [1, 256, 256, 256]> input_159 = silu(x = add_43)[name = tensor<string, []>("input_159")];
            tensor<int32, [2]> var_483 = const()[name = tensor<string, []>("op_483"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 256, 256, 256]> input_161 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias, dilations = var_485, groups = var_26, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight, x = input_159)[name = tensor<string, []>("input_161")];
            tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
            tensor<fp32, [1, 32, 8, 256, 256]> reshape_88 = reshape(shape = reshape_88_shape_0, x = input_161)[name = tensor<string, []>("reshape_88")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = tensor<string, []>("reduce_mean_66")];
            tensor<fp32, [1, 32, 8, 256, 256]> sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = tensor<string, []>("sub_44")];
            tensor<fp32, [1, 32, 8, 256, 256]> square_22 = square(x = sub_44)[name = tensor<string, []>("square_22")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = tensor<string, []>("reduce_mean_68")];
            tensor<fp32, []> add_44_y_0 = const()[name = tensor<string, []>("add_44_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = tensor<string, []>("add_44")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_22 = sqrt(x = add_44)[name = tensor<string, []>("sqrt_22")];
            tensor<fp32, [1, 32, 8, 256, 256]> real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = tensor<string, []>("real_div_22")];
            tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
            tensor<fp32, [1, 256, 256, 256]> reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = tensor<string, []>("reshape_89")];
            tensor<fp32, [256]> add_45_gamma_0 = const()[name = tensor<string, []>("add_45_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197964800)))];
            tensor<fp32, [256]> add_45_beta_0 = const()[name = tensor<string, []>("add_45_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197965888)))];
            tensor<fp32, []> add_45_epsilon_0 = const()[name = tensor<string, []>("add_45_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 256, 256]> add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_89)[name = tensor<string, []>("add_45")];
            tensor<fp32, [1, 256, 256, 256]> input_165 = silu(x = add_45)[name = tensor<string, []>("input_165")];
            tensor<int32, [2]> var_495 = const()[name = tensor<string, []>("op_495"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_497 = const()[name = tensor<string, []>("op_497"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_39_pad_type_0 = const()[name = tensor<string, []>("hidden_states_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = tensor<string, []>("hidden_states_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 256, 256, 256]> hidden_states_39 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias, dilations = var_497, groups = var_26, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight, x = input_165)[name = tensor<string, []>("hidden_states_39")];
            tensor<fp32, [1, 256, 256, 256]> var_500 = add(x = var_470, y = hidden_states_39)[name = tensor<string, []>("op_500")];
            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<fp32, [1, 256, 512, 512]> input_169 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500)[name = tensor<string, []>("input_169")];
            tensor<int32, [2]> var_508 = const()[name = tensor<string, []>("op_508"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_510 = const()[name = tensor<string, []>("op_510"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 256, 512, 512]> input_171 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = var_510, groups = var_26, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_169)[name = tensor<string, []>("input_171")];
            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])];
            tensor<fp32, [1, 32, 8, 512, 512]> reshape_92 = reshape(shape = reshape_92_shape_0, x = input_171)[name = tensor<string, []>("reshape_92")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = tensor<string, []>("reduce_mean_69")];
            tensor<fp32, [1, 32, 8, 512, 512]> sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = tensor<string, []>("sub_46")];
            tensor<fp32, [1, 32, 8, 512, 512]> square_23 = square(x = sub_46)[name = tensor<string, []>("square_23")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = tensor<string, []>("reduce_mean_71")];
            tensor<fp32, []> add_46_y_0 = const()[name = tensor<string, []>("add_46_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = tensor<string, []>("add_46")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_23 = sqrt(x = add_46)[name = tensor<string, []>("sqrt_23")];
            tensor<fp32, [1, 32, 8, 512, 512]> real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = tensor<string, []>("real_div_23")];
            tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])];
            tensor<fp32, [1, 256, 512, 512]> reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = tensor<string, []>("reshape_93")];
            tensor<fp32, [256]> add_47_gamma_0 = const()[name = tensor<string, []>("add_47_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197966976)))];
            tensor<fp32, [256]> add_47_beta_0 = const()[name = tensor<string, []>("add_47_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197968064)))];
            tensor<fp32, []> add_47_epsilon_0 = const()[name = tensor<string, []>("add_47_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 512, 512]> add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_93)[name = tensor<string, []>("add_47")];
            tensor<fp32, [1, 256, 512, 512]> input_175 = silu(x = add_47)[name = tensor<string, []>("input_175")];
            tensor<int32, [2]> var_530 = const()[name = tensor<string, []>("op_530"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_532 = const()[name = tensor<string, []>("op_532"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 128, 512, 512]> input_177 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias, dilations = var_532, groups = var_26, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight, x = input_175)[name = tensor<string, []>("input_177")];
            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_96 = reshape(shape = reshape_96_shape_0, x = input_177)[name = tensor<string, []>("reshape_96")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = tensor<string, []>("reduce_mean_72")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = tensor<string, []>("sub_48")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_24 = square(x = sub_48)[name = tensor<string, []>("square_24")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = tensor<string, []>("reduce_mean_74")];
            tensor<fp32, []> add_48_y_0 = const()[name = tensor<string, []>("add_48_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = tensor<string, []>("add_48")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_24 = sqrt(x = add_48)[name = tensor<string, []>("sqrt_24")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = tensor<string, []>("real_div_24")];
            tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = tensor<string, []>("reshape_97")];
            tensor<fp32, [128]> add_49_mean_0 = const()[name = tensor<string, []>("add_49_mean_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197969152)))];
            tensor<fp32, [128]> add_49_variance_0 = const()[name = tensor<string, []>("add_49_variance_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197969728)))];
            tensor<fp32, [128]> add_49_gamma_0 = const()[name = tensor<string, []>("add_49_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197970304)))];
            tensor<fp32, [128]> add_49_beta_0 = const()[name = tensor<string, []>("add_49_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197970880)))];
            tensor<fp32, []> add_49_epsilon_0 = const()[name = tensor<string, []>("add_49_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_97)[name = tensor<string, []>("add_49")];
            tensor<fp32, [1, 128, 512, 512]> input_181 = silu(x = add_49)[name = tensor<string, []>("input_181")];
            tensor<int32, [2]> var_542 = const()[name = tensor<string, []>("op_542"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_544 = const()[name = tensor<string, []>("op_544"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 128, 512, 512]> hidden_states_43 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias, dilations = var_544, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight, x = input_181)[name = tensor<string, []>("hidden_states_43")];
            tensor<int32, [2]> var_549 = const()[name = tensor<string, []>("op_549"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_551 = const()[name = tensor<string, []>("op_551"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_tensor_pad_type_0 = const()[name = tensor<string, []>("input_tensor_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_tensor_pad_0 = const()[name = tensor<string, []>("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp32, [1, 128, 512, 512]> input_tensor = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_171)[name = tensor<string, []>("input_tensor")];
            tensor<fp32, [1, 128, 512, 512]> var_554 = add(x = input_tensor, y = hidden_states_43)[name = tensor<string, []>("op_554")];
            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_100 = reshape(shape = reshape_100_shape_0, x = var_554)[name = tensor<string, []>("reshape_100")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor<string, []>("reduce_mean_75")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = tensor<string, []>("sub_50")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_25 = square(x = sub_50)[name = tensor<string, []>("square_25")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = tensor<string, []>("reduce_mean_77")];
            tensor<fp32, []> add_50_y_0 = const()[name = tensor<string, []>("add_50_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = tensor<string, []>("add_50")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_25 = sqrt(x = add_50)[name = tensor<string, []>("sqrt_25")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = tensor<string, []>("real_div_25")];
            tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = tensor<string, []>("reshape_101")];
            tensor<fp32, [128]> add_51_gamma_0 = const()[name = tensor<string, []>("add_51_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197971456)))];
            tensor<fp32, [128]> add_51_beta_0 = const()[name = tensor<string, []>("add_51_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197972032)))];
            tensor<fp32, []> add_51_epsilon_0 = const()[name = tensor<string, []>("add_51_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_101)[name = tensor<string, []>("add_51")];
            tensor<fp32, [1, 128, 512, 512]> input_189 = silu(x = add_51)[name = tensor<string, []>("input_189")];
            tensor<int32, [2]> var_567 = const()[name = tensor<string, []>("op_567"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_569 = const()[name = tensor<string, []>("op_569"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 128, 512, 512]> input_191 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias, dilations = var_569, groups = var_26, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight, x = input_189)[name = tensor<string, []>("input_191")];
            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_104 = reshape(shape = reshape_104_shape_0, x = input_191)[name = tensor<string, []>("reshape_104")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = tensor<string, []>("reduce_mean_78")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = tensor<string, []>("sub_52")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_26 = square(x = sub_52)[name = tensor<string, []>("square_26")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = tensor<string, []>("reduce_mean_80")];
            tensor<fp32, []> add_52_y_0 = const()[name = tensor<string, []>("add_52_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = tensor<string, []>("add_52")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_26 = sqrt(x = add_52)[name = tensor<string, []>("sqrt_26")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = tensor<string, []>("real_div_26")];
            tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = tensor<string, []>("reshape_105")];
            tensor<fp32, [128]> add_53_gamma_0 = const()[name = tensor<string, []>("add_53_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197972608)))];
            tensor<fp32, [128]> add_53_beta_0 = const()[name = tensor<string, []>("add_53_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197973184)))];
            tensor<fp32, []> add_53_epsilon_0 = const()[name = tensor<string, []>("add_53_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_105)[name = tensor<string, []>("add_53")];
            tensor<fp32, [1, 128, 512, 512]> input_195 = silu(x = add_53)[name = tensor<string, []>("input_195")];
            tensor<int32, [2]> var_579 = const()[name = tensor<string, []>("op_579"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_581 = const()[name = tensor<string, []>("op_581"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 128, 512, 512]> hidden_states_45 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias, dilations = var_581, groups = var_26, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight, x = input_195)[name = tensor<string, []>("hidden_states_45")];
            tensor<fp32, [1, 128, 512, 512]> var_584 = add(x = var_554, y = hidden_states_45)[name = tensor<string, []>("op_584")];
            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_108 = reshape(shape = reshape_108_shape_0, x = var_584)[name = tensor<string, []>("reshape_108")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor<string, []>("reduce_mean_81")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = tensor<string, []>("sub_54")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_27 = square(x = sub_54)[name = tensor<string, []>("square_27")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = tensor<string, []>("reduce_mean_83")];
            tensor<fp32, []> add_54_y_0 = const()[name = tensor<string, []>("add_54_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = tensor<string, []>("add_54")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_27 = sqrt(x = add_54)[name = tensor<string, []>("sqrt_27")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = tensor<string, []>("real_div_27")];
            tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = tensor<string, []>("reshape_109")];
            tensor<fp32, [128]> add_55_gamma_0 = const()[name = tensor<string, []>("add_55_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197973760)))];
            tensor<fp32, [128]> add_55_beta_0 = const()[name = tensor<string, []>("add_55_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197974336)))];
            tensor<fp32, []> add_55_epsilon_0 = const()[name = tensor<string, []>("add_55_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_109)[name = tensor<string, []>("add_55")];
            tensor<fp32, [1, 128, 512, 512]> input_203 = silu(x = add_55)[name = tensor<string, []>("input_203")];
            tensor<int32, [2]> var_597 = const()[name = tensor<string, []>("op_597"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_599 = const()[name = tensor<string, []>("op_599"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 128, 512, 512]> input_205 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias, dilations = var_599, groups = var_26, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight, x = input_203)[name = tensor<string, []>("input_205")];
            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_112 = reshape(shape = reshape_112_shape_0, x = input_205)[name = tensor<string, []>("reshape_112")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = tensor<string, []>("reduce_mean_84")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = tensor<string, []>("sub_56")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_28 = square(x = sub_56)[name = tensor<string, []>("square_28")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = tensor<string, []>("reduce_mean_86")];
            tensor<fp32, []> add_56_y_0 = const()[name = tensor<string, []>("add_56_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = tensor<string, []>("add_56")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_28 = sqrt(x = add_56)[name = tensor<string, []>("sqrt_28")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = tensor<string, []>("real_div_28")];
            tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = tensor<string, []>("reshape_113")];
            tensor<fp32, [128]> add_57_gamma_0 = const()[name = tensor<string, []>("add_57_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197974912)))];
            tensor<fp32, [128]> add_57_beta_0 = const()[name = tensor<string, []>("add_57_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197975488)))];
            tensor<fp32, []> add_57_epsilon_0 = const()[name = tensor<string, []>("add_57_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_113)[name = tensor<string, []>("add_57")];
            tensor<fp32, [1, 128, 512, 512]> input_209 = silu(x = add_57)[name = tensor<string, []>("input_209")];
            tensor<int32, [2]> var_609 = const()[name = tensor<string, []>("op_609"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_611 = const()[name = tensor<string, []>("op_611"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp32, [1, 128, 512, 512]> hidden_states = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight, x = input_209)[name = tensor<string, []>("hidden_states")];
            tensor<fp32, [1, 128, 512, 512]> var_614 = add(x = var_584, y = hidden_states)[name = tensor<string, []>("op_614")];
            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
            tensor<fp32, [1, 32, 4, 512, 512]> reshape_116 = reshape(shape = reshape_116_shape_0, x = var_614)[name = tensor<string, []>("reshape_116")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor<string, []>("reduce_mean_87")];
            tensor<fp32, [1, 32, 4, 512, 512]> sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = tensor<string, []>("sub_58")];
            tensor<fp32, [1, 32, 4, 512, 512]> square_29 = square(x = sub_58)[name = tensor<string, []>("square_29")];
            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<fp32, [1, 32, 1, 1, 1]> reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = tensor<string, []>("reduce_mean_89")];
            tensor<fp32, []> add_58_y_0 = const()[name = tensor<string, []>("add_58_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)];
            tensor<fp32, [1, 32, 1, 1, 1]> add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = tensor<string, []>("add_58")];
            tensor<fp32, [1, 32, 1, 1, 1]> sqrt_29 = sqrt(x = add_58)[name = tensor<string, []>("sqrt_29")];
            tensor<fp32, [1, 32, 4, 512, 512]> real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = tensor<string, []>("real_div_29")];
            tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
            tensor<fp32, [1, 128, 512, 512]> reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = tensor<string, []>("reshape_117")];
            tensor<fp32, [128]> add_59_gamma_0 = const()[name = tensor<string, []>("add_59_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197976064)))];
            tensor<fp32, [128]> add_59_beta_0 = const()[name = tensor<string, []>("add_59_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197976640)))];
            tensor<fp32, []> add_59_epsilon_0 = const()[name = tensor<string, []>("add_59_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 128, 512, 512]> add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_117)[name = tensor<string, []>("add_59")];
            tensor<fp32, [1, 128, 512, 512]> input = silu(x = add_59)[name = tensor<string, []>("input")];
            tensor<int32, [2]> var_623 = const()[name = tensor<string, []>("op_623"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_625 = const()[name = tensor<string, []>("op_625"), val = tensor<int32, [2]>([1, 1])];
            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<fp32, [1, 3, 512, 512]> image = conv(bias = decoder_conv_out_bias, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight, x = input)[name = tensor<string, []>("op_627")];
        } -> (image);
}