Hi @SunilJB, The graph can be printed as below:
graph torch-jit-export (
%input.1[FLOAT, 1x3x320x192]
) initializers (
%module_list.0.BatchNorm2d.bias[FLOAT, 32]
%module_list.0.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.0.BatchNorm2d.running_mean[FLOAT, 32]
%module_list.0.BatchNorm2d.running_var[FLOAT, 32]
%module_list.0.BatchNorm2d.weight[FLOAT, 32]
%module_list.0.Conv2d.weight[FLOAT, 32x3x3x3]
%module_list.1.BatchNorm2d.bias[FLOAT, 64]
%module_list.1.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.1.BatchNorm2d.running_mean[FLOAT, 64]
%module_list.1.BatchNorm2d.running_var[FLOAT, 64]
%module_list.1.BatchNorm2d.weight[FLOAT, 64]
%module_list.1.Conv2d.weight[FLOAT, 64x32x3x3]
%module_list.10.BatchNorm2d.bias[FLOAT, 128]
%module_list.10.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.10.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.10.BatchNorm2d.running_var[FLOAT, 128]
%module_list.10.BatchNorm2d.weight[FLOAT, 128]
%module_list.10.Conv2d.weight[FLOAT, 128x64x3x3]
%module_list.100.Conv2d.bias[FLOAT, 18]
%module_list.100.Conv2d.weight[FLOAT, 18x512x1x1]
%module_list.103.BatchNorm2d.bias[FLOAT, 128]
%module_list.103.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.103.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.103.BatchNorm2d.running_var[FLOAT, 128]
%module_list.103.BatchNorm2d.weight[FLOAT, 128]
%module_list.103.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.106.BatchNorm2d.bias[FLOAT, 128]
%module_list.106.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.106.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.106.BatchNorm2d.running_var[FLOAT, 128]
%module_list.106.BatchNorm2d.weight[FLOAT, 128]
%module_list.106.Conv2d.weight[FLOAT, 128x384x1x1]
%module_list.107.BatchNorm2d.bias[FLOAT, 256]
%module_list.107.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.107.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.107.BatchNorm2d.running_var[FLOAT, 256]
%module_list.107.BatchNorm2d.weight[FLOAT, 256]
%module_list.107.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.108.BatchNorm2d.bias[FLOAT, 128]
%module_list.108.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.108.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.108.BatchNorm2d.running_var[FLOAT, 128]
%module_list.108.BatchNorm2d.weight[FLOAT, 128]
%module_list.108.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.109.BatchNorm2d.bias[FLOAT, 256]
%module_list.109.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.109.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.109.BatchNorm2d.running_var[FLOAT, 256]
%module_list.109.BatchNorm2d.weight[FLOAT, 256]
%module_list.109.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.110.BatchNorm2d.bias[FLOAT, 128]
%module_list.110.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.110.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.110.BatchNorm2d.running_var[FLOAT, 128]
%module_list.110.BatchNorm2d.weight[FLOAT, 128]
%module_list.110.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.111.BatchNorm2d.bias[FLOAT, 256]
%module_list.111.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.111.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.111.BatchNorm2d.running_var[FLOAT, 256]
%module_list.111.BatchNorm2d.weight[FLOAT, 256]
%module_list.111.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.112.Conv2d.bias[FLOAT, 18]
%module_list.112.Conv2d.weight[FLOAT, 18x256x1x1]
%module_list.12.BatchNorm2d.bias[FLOAT, 256]
%module_list.12.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.12.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.12.BatchNorm2d.running_var[FLOAT, 256]
%module_list.12.BatchNorm2d.weight[FLOAT, 256]
%module_list.12.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.13.BatchNorm2d.bias[FLOAT, 128]
%module_list.13.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.13.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.13.BatchNorm2d.running_var[FLOAT, 128]
%module_list.13.BatchNorm2d.weight[FLOAT, 128]
%module_list.13.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.14.BatchNorm2d.bias[FLOAT, 256]
%module_list.14.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.14.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.14.BatchNorm2d.running_var[FLOAT, 256]
%module_list.14.BatchNorm2d.weight[FLOAT, 256]
%module_list.14.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.16.BatchNorm2d.bias[FLOAT, 128]
%module_list.16.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.16.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.16.BatchNorm2d.running_var[FLOAT, 128]
%module_list.16.BatchNorm2d.weight[FLOAT, 128]
%module_list.16.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.17.BatchNorm2d.bias[FLOAT, 256]
%module_list.17.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.17.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.17.BatchNorm2d.running_var[FLOAT, 256]
%module_list.17.BatchNorm2d.weight[FLOAT, 256]
%module_list.17.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.19.BatchNorm2d.bias[FLOAT, 128]
%module_list.19.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.19.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.19.BatchNorm2d.running_var[FLOAT, 128]
%module_list.19.BatchNorm2d.weight[FLOAT, 128]
%module_list.19.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.2.BatchNorm2d.bias[FLOAT, 32]
%module_list.2.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.2.BatchNorm2d.running_mean[FLOAT, 32]
%module_list.2.BatchNorm2d.running_var[FLOAT, 32]
%module_list.2.BatchNorm2d.weight[FLOAT, 32]
%module_list.2.Conv2d.weight[FLOAT, 32x64x1x1]
%module_list.20.BatchNorm2d.bias[FLOAT, 256]
%module_list.20.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.20.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.20.BatchNorm2d.running_var[FLOAT, 256]
%module_list.20.BatchNorm2d.weight[FLOAT, 256]
%module_list.20.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.22.BatchNorm2d.bias[FLOAT, 128]
%module_list.22.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.22.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.22.BatchNorm2d.running_var[FLOAT, 128]
%module_list.22.BatchNorm2d.weight[FLOAT, 128]
%module_list.22.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.23.BatchNorm2d.bias[FLOAT, 256]
%module_list.23.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.23.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.23.BatchNorm2d.running_var[FLOAT, 256]
%module_list.23.BatchNorm2d.weight[FLOAT, 256]
%module_list.23.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.25.BatchNorm2d.bias[FLOAT, 128]
%module_list.25.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.25.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.25.BatchNorm2d.running_var[FLOAT, 128]
%module_list.25.BatchNorm2d.weight[FLOAT, 128]
%module_list.25.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.26.BatchNorm2d.bias[FLOAT, 256]
%module_list.26.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.26.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.26.BatchNorm2d.running_var[FLOAT, 256]
%module_list.26.BatchNorm2d.weight[FLOAT, 256]
%module_list.26.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.28.BatchNorm2d.bias[FLOAT, 128]
%module_list.28.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.28.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.28.BatchNorm2d.running_var[FLOAT, 128]
%module_list.28.BatchNorm2d.weight[FLOAT, 128]
%module_list.28.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.29.BatchNorm2d.bias[FLOAT, 256]
%module_list.29.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.29.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.29.BatchNorm2d.running_var[FLOAT, 256]
%module_list.29.BatchNorm2d.weight[FLOAT, 256]
%module_list.29.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.3.BatchNorm2d.bias[FLOAT, 64]
%module_list.3.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.3.BatchNorm2d.running_mean[FLOAT, 64]
%module_list.3.BatchNorm2d.running_var[FLOAT, 64]
%module_list.3.BatchNorm2d.weight[FLOAT, 64]
%module_list.3.Conv2d.weight[FLOAT, 64x32x3x3]
%module_list.31.BatchNorm2d.bias[FLOAT, 128]
%module_list.31.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.31.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.31.BatchNorm2d.running_var[FLOAT, 128]
%module_list.31.BatchNorm2d.weight[FLOAT, 128]
%module_list.31.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.32.BatchNorm2d.bias[FLOAT, 256]
%module_list.32.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.32.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.32.BatchNorm2d.running_var[FLOAT, 256]
%module_list.32.BatchNorm2d.weight[FLOAT, 256]
%module_list.32.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.34.BatchNorm2d.bias[FLOAT, 128]
%module_list.34.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.34.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.34.BatchNorm2d.running_var[FLOAT, 128]
%module_list.34.BatchNorm2d.weight[FLOAT, 128]
%module_list.34.Conv2d.weight[FLOAT, 128x256x1x1]
%module_list.35.BatchNorm2d.bias[FLOAT, 256]
%module_list.35.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.35.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.35.BatchNorm2d.running_var[FLOAT, 256]
%module_list.35.BatchNorm2d.weight[FLOAT, 256]
%module_list.35.Conv2d.weight[FLOAT, 256x128x3x3]
%module_list.37.BatchNorm2d.bias[FLOAT, 512]
%module_list.37.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.37.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.37.BatchNorm2d.running_var[FLOAT, 512]
%module_list.37.BatchNorm2d.weight[FLOAT, 512]
%module_list.37.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.38.BatchNorm2d.bias[FLOAT, 256]
%module_list.38.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.38.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.38.BatchNorm2d.running_var[FLOAT, 256]
%module_list.38.BatchNorm2d.weight[FLOAT, 256]
%module_list.38.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.39.BatchNorm2d.bias[FLOAT, 512]
%module_list.39.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.39.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.39.BatchNorm2d.running_var[FLOAT, 512]
%module_list.39.BatchNorm2d.weight[FLOAT, 512]
%module_list.39.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.41.BatchNorm2d.bias[FLOAT, 256]
%module_list.41.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.41.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.41.BatchNorm2d.running_var[FLOAT, 256]
%module_list.41.BatchNorm2d.weight[FLOAT, 256]
%module_list.41.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.42.BatchNorm2d.bias[FLOAT, 512]
%module_list.42.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.42.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.42.BatchNorm2d.running_var[FLOAT, 512]
%module_list.42.BatchNorm2d.weight[FLOAT, 512]
%module_list.42.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.44.BatchNorm2d.bias[FLOAT, 256]
%module_list.44.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.44.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.44.BatchNorm2d.running_var[FLOAT, 256]
%module_list.44.BatchNorm2d.weight[FLOAT, 256]
%module_list.44.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.45.BatchNorm2d.bias[FLOAT, 512]
%module_list.45.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.45.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.45.BatchNorm2d.running_var[FLOAT, 512]
%module_list.45.BatchNorm2d.weight[FLOAT, 512]
%module_list.45.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.47.BatchNorm2d.bias[FLOAT, 256]
%module_list.47.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.47.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.47.BatchNorm2d.running_var[FLOAT, 256]
%module_list.47.BatchNorm2d.weight[FLOAT, 256]
%module_list.47.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.48.BatchNorm2d.bias[FLOAT, 512]
%module_list.48.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.48.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.48.BatchNorm2d.running_var[FLOAT, 512]
%module_list.48.BatchNorm2d.weight[FLOAT, 512]
%module_list.48.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.5.BatchNorm2d.bias[FLOAT, 128]
%module_list.5.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.5.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.5.BatchNorm2d.running_var[FLOAT, 128]
%module_list.5.BatchNorm2d.weight[FLOAT, 128]
%module_list.5.Conv2d.weight[FLOAT, 128x64x3x3]
%module_list.50.BatchNorm2d.bias[FLOAT, 256]
%module_list.50.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.50.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.50.BatchNorm2d.running_var[FLOAT, 256]
%module_list.50.BatchNorm2d.weight[FLOAT, 256]
%module_list.50.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.51.BatchNorm2d.bias[FLOAT, 512]
%module_list.51.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.51.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.51.BatchNorm2d.running_var[FLOAT, 512]
%module_list.51.BatchNorm2d.weight[FLOAT, 512]
%module_list.51.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.53.BatchNorm2d.bias[FLOAT, 256]
%module_list.53.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.53.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.53.BatchNorm2d.running_var[FLOAT, 256]
%module_list.53.BatchNorm2d.weight[FLOAT, 256]
%module_list.53.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.54.BatchNorm2d.bias[FLOAT, 512]
%module_list.54.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.54.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.54.BatchNorm2d.running_var[FLOAT, 512]
%module_list.54.BatchNorm2d.weight[FLOAT, 512]
%module_list.54.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.56.BatchNorm2d.bias[FLOAT, 256]
%module_list.56.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.56.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.56.BatchNorm2d.running_var[FLOAT, 256]
%module_list.56.BatchNorm2d.weight[FLOAT, 256]
%module_list.56.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.57.BatchNorm2d.bias[FLOAT, 512]
%module_list.57.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.57.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.57.BatchNorm2d.running_var[FLOAT, 512]
%module_list.57.BatchNorm2d.weight[FLOAT, 512]
%module_list.57.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.59.BatchNorm2d.bias[FLOAT, 256]
%module_list.59.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.59.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.59.BatchNorm2d.running_var[FLOAT, 256]
%module_list.59.BatchNorm2d.weight[FLOAT, 256]
%module_list.59.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.6.BatchNorm2d.bias[FLOAT, 64]
%module_list.6.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.6.BatchNorm2d.running_mean[FLOAT, 64]
%module_list.6.BatchNorm2d.running_var[FLOAT, 64]
%module_list.6.BatchNorm2d.weight[FLOAT, 64]
%module_list.6.Conv2d.weight[FLOAT, 64x128x1x1]
%module_list.60.BatchNorm2d.bias[FLOAT, 512]
%module_list.60.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.60.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.60.BatchNorm2d.running_var[FLOAT, 512]
%module_list.60.BatchNorm2d.weight[FLOAT, 512]
%module_list.60.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.62.BatchNorm2d.bias[FLOAT, 1024]
%module_list.62.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.62.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.62.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.62.BatchNorm2d.weight[FLOAT, 1024]
%module_list.62.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.63.BatchNorm2d.bias[FLOAT, 512]
%module_list.63.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.63.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.63.BatchNorm2d.running_var[FLOAT, 512]
%module_list.63.BatchNorm2d.weight[FLOAT, 512]
%module_list.63.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.64.BatchNorm2d.bias[FLOAT, 1024]
%module_list.64.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.64.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.64.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.64.BatchNorm2d.weight[FLOAT, 1024]
%module_list.64.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.66.BatchNorm2d.bias[FLOAT, 512]
%module_list.66.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.66.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.66.BatchNorm2d.running_var[FLOAT, 512]
%module_list.66.BatchNorm2d.weight[FLOAT, 512]
%module_list.66.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.67.BatchNorm2d.bias[FLOAT, 1024]
%module_list.67.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.67.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.67.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.67.BatchNorm2d.weight[FLOAT, 1024]
%module_list.67.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.69.BatchNorm2d.bias[FLOAT, 512]
%module_list.69.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.69.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.69.BatchNorm2d.running_var[FLOAT, 512]
%module_list.69.BatchNorm2d.weight[FLOAT, 512]
%module_list.69.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.7.BatchNorm2d.bias[FLOAT, 128]
%module_list.7.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.7.BatchNorm2d.running_mean[FLOAT, 128]
%module_list.7.BatchNorm2d.running_var[FLOAT, 128]
%module_list.7.BatchNorm2d.weight[FLOAT, 128]
%module_list.7.Conv2d.weight[FLOAT, 128x64x3x3]
%module_list.70.BatchNorm2d.bias[FLOAT, 1024]
%module_list.70.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.70.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.70.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.70.BatchNorm2d.weight[FLOAT, 1024]
%module_list.70.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.72.BatchNorm2d.bias[FLOAT, 512]
%module_list.72.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.72.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.72.BatchNorm2d.running_var[FLOAT, 512]
%module_list.72.BatchNorm2d.weight[FLOAT, 512]
%module_list.72.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.73.BatchNorm2d.bias[FLOAT, 1024]
%module_list.73.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.73.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.73.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.73.BatchNorm2d.weight[FLOAT, 1024]
%module_list.73.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.75.BatchNorm2d.bias[FLOAT, 512]
%module_list.75.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.75.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.75.BatchNorm2d.running_var[FLOAT, 512]
%module_list.75.BatchNorm2d.weight[FLOAT, 512]
%module_list.75.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.76.BatchNorm2d.bias[FLOAT, 1024]
%module_list.76.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.76.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.76.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.76.BatchNorm2d.weight[FLOAT, 1024]
%module_list.76.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.77.BatchNorm2d.bias[FLOAT, 512]
%module_list.77.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.77.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.77.BatchNorm2d.running_var[FLOAT, 512]
%module_list.77.BatchNorm2d.weight[FLOAT, 512]
%module_list.77.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.84.BatchNorm2d.bias[FLOAT, 512]
%module_list.84.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.84.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.84.BatchNorm2d.running_var[FLOAT, 512]
%module_list.84.BatchNorm2d.weight[FLOAT, 512]
%module_list.84.Conv2d.weight[FLOAT, 512x2048x1x1]
%module_list.85.BatchNorm2d.bias[FLOAT, 1024]
%module_list.85.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.85.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.85.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.85.BatchNorm2d.weight[FLOAT, 1024]
%module_list.85.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.86.BatchNorm2d.bias[FLOAT, 512]
%module_list.86.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.86.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.86.BatchNorm2d.running_var[FLOAT, 512]
%module_list.86.BatchNorm2d.weight[FLOAT, 512]
%module_list.86.Conv2d.weight[FLOAT, 512x1024x1x1]
%module_list.87.BatchNorm2d.bias[FLOAT, 1024]
%module_list.87.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.87.BatchNorm2d.running_mean[FLOAT, 1024]
%module_list.87.BatchNorm2d.running_var[FLOAT, 1024]
%module_list.87.BatchNorm2d.weight[FLOAT, 1024]
%module_list.87.Conv2d.weight[FLOAT, 1024x512x3x3]
%module_list.88.Conv2d.bias[FLOAT, 18]
%module_list.88.Conv2d.weight[FLOAT, 18x1024x1x1]
%module_list.9.BatchNorm2d.bias[FLOAT, 64]
%module_list.9.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.9.BatchNorm2d.running_mean[FLOAT, 64]
%module_list.9.BatchNorm2d.running_var[FLOAT, 64]
%module_list.9.BatchNorm2d.weight[FLOAT, 64]
%module_list.9.Conv2d.weight[FLOAT, 64x128x1x1]
%module_list.91.BatchNorm2d.bias[FLOAT, 256]
%module_list.91.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.91.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.91.BatchNorm2d.running_var[FLOAT, 256]
%module_list.91.BatchNorm2d.weight[FLOAT, 256]
%module_list.91.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.94.BatchNorm2d.bias[FLOAT, 256]
%module_list.94.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.94.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.94.BatchNorm2d.running_var[FLOAT, 256]
%module_list.94.BatchNorm2d.weight[FLOAT, 256]
%module_list.94.Conv2d.weight[FLOAT, 256x768x1x1]
%module_list.95.BatchNorm2d.bias[FLOAT, 512]
%module_list.95.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.95.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.95.BatchNorm2d.running_var[FLOAT, 512]
%module_list.95.BatchNorm2d.weight[FLOAT, 512]
%module_list.95.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.96.BatchNorm2d.bias[FLOAT, 256]
%module_list.96.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.96.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.96.BatchNorm2d.running_var[FLOAT, 256]
%module_list.96.BatchNorm2d.weight[FLOAT, 256]
%module_list.96.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.97.BatchNorm2d.bias[FLOAT, 512]
%module_list.97.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.97.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.97.BatchNorm2d.running_var[FLOAT, 512]
%module_list.97.BatchNorm2d.weight[FLOAT, 512]
%module_list.97.Conv2d.weight[FLOAT, 512x256x3x3]
%module_list.98.BatchNorm2d.bias[FLOAT, 256]
%module_list.98.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.98.BatchNorm2d.running_mean[FLOAT, 256]
%module_list.98.BatchNorm2d.running_var[FLOAT, 256]
%module_list.98.BatchNorm2d.weight[FLOAT, 256]
%module_list.98.Conv2d.weight[FLOAT, 256x512x1x1]
%module_list.99.BatchNorm2d.bias[FLOAT, 512]
%module_list.99.BatchNorm2d.num_batches_tracked[INT64, scalar]
%module_list.99.BatchNorm2d.running_mean[FLOAT, 512]
%module_list.99.BatchNorm2d.running_var[FLOAT, 512]
%module_list.99.BatchNorm2d.weight[FLOAT, 512]
%module_list.99.Conv2d.weight[FLOAT, 512x256x3x3]
) {
%445 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%input.1, %module_list.0.Conv2d.weight)
%446 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%445, %module_list.0.BatchNorm2d.weight, %module_list.0.BatchNorm2d.bias, %module_list.0.BatchNorm2d.running_mean, %module_list.0.BatchNorm2d.running_var)
%447 = LeakyRelualpha = 0.100000001490116
%448 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%447, %module_list.1.Conv2d.weight)
%449 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%448, %module_list.1.BatchNorm2d.weight, %module_list.1.BatchNorm2d.bias, %module_list.1.BatchNorm2d.running_mean, %module_list.1.BatchNorm2d.running_var)
%450 = LeakyRelualpha = 0.100000001490116
%451 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%450, %module_list.2.Conv2d.weight)
%452 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%451, %module_list.2.BatchNorm2d.weight, %module_list.2.BatchNorm2d.bias, %module_list.2.BatchNorm2d.running_mean, %module_list.2.BatchNorm2d.running_var)
%453 = LeakyRelualpha = 0.100000001490116
%454 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%453, %module_list.3.Conv2d.weight)
%455 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%454, %module_list.3.BatchNorm2d.weight, %module_list.3.BatchNorm2d.bias, %module_list.3.BatchNorm2d.running_mean, %module_list.3.BatchNorm2d.running_var)
%456 = LeakyRelualpha = 0.100000001490116
%457 = Add(%456, %450)
%458 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%457, %module_list.5.Conv2d.weight)
%459 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%458, %module_list.5.BatchNorm2d.weight, %module_list.5.BatchNorm2d.bias, %module_list.5.BatchNorm2d.running_mean, %module_list.5.BatchNorm2d.running_var)
%460 = LeakyRelualpha = 0.100000001490116
%461 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%460, %module_list.6.Conv2d.weight)
%462 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%461, %module_list.6.BatchNorm2d.weight, %module_list.6.BatchNorm2d.bias, %module_list.6.BatchNorm2d.running_mean, %module_list.6.BatchNorm2d.running_var)
%463 = LeakyRelualpha = 0.100000001490116
%464 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%463, %module_list.7.Conv2d.weight)
%465 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%464, %module_list.7.BatchNorm2d.weight, %module_list.7.BatchNorm2d.bias, %module_list.7.BatchNorm2d.running_mean, %module_list.7.BatchNorm2d.running_var)
%466 = LeakyRelualpha = 0.100000001490116
%467 = Add(%466, %460)
%468 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%467, %module_list.9.Conv2d.weight)
%469 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%468, %module_list.9.BatchNorm2d.weight, %module_list.9.BatchNorm2d.bias, %module_list.9.BatchNorm2d.running_mean, %module_list.9.BatchNorm2d.running_var)
%470 = LeakyRelualpha = 0.100000001490116
%471 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%470, %module_list.10.Conv2d.weight)
%472 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%471, %module_list.10.BatchNorm2d.weight, %module_list.10.BatchNorm2d.bias, %module_list.10.BatchNorm2d.running_mean, %module_list.10.BatchNorm2d.running_var)
%473 = LeakyRelualpha = 0.100000001490116
%474 = Add(%473, %467)
%475 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%474, %module_list.12.Conv2d.weight)
%476 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%475, %module_list.12.BatchNorm2d.weight, %module_list.12.BatchNorm2d.bias, %module_list.12.BatchNorm2d.running_mean, %module_list.12.BatchNorm2d.running_var)
%477 = LeakyRelualpha = 0.100000001490116
%478 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%477, %module_list.13.Conv2d.weight)
%479 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%478, %module_list.13.BatchNorm2d.weight, %module_list.13.BatchNorm2d.bias, %module_list.13.BatchNorm2d.running_mean, %module_list.13.BatchNorm2d.running_var)
%480 = LeakyRelualpha = 0.100000001490116
%481 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%480, %module_list.14.Conv2d.weight)
%482 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%481, %module_list.14.BatchNorm2d.weight, %module_list.14.BatchNorm2d.bias, %module_list.14.BatchNorm2d.running_mean, %module_list.14.BatchNorm2d.running_var)
%483 = LeakyRelualpha = 0.100000001490116
%484 = Add(%483, %477)
%485 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%484, %module_list.16.Conv2d.weight)
%486 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%485, %module_list.16.BatchNorm2d.weight, %module_list.16.BatchNorm2d.bias, %module_list.16.BatchNorm2d.running_mean, %module_list.16.BatchNorm2d.running_var)
%487 = LeakyRelualpha = 0.100000001490116
%488 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%487, %module_list.17.Conv2d.weight)
%489 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%488, %module_list.17.BatchNorm2d.weight, %module_list.17.BatchNorm2d.bias, %module_list.17.BatchNorm2d.running_mean, %module_list.17.BatchNorm2d.running_var)
%490 = LeakyRelualpha = 0.100000001490116
%491 = Add(%490, %484)
%492 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%491, %module_list.19.Conv2d.weight)
%493 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%492, %module_list.19.BatchNorm2d.weight, %module_list.19.BatchNorm2d.bias, %module_list.19.BatchNorm2d.running_mean, %module_list.19.BatchNorm2d.running_var)
%494 = LeakyRelualpha = 0.100000001490116
%495 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%494, %module_list.20.Conv2d.weight)
%496 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%495, %module_list.20.BatchNorm2d.weight, %module_list.20.BatchNorm2d.bias, %module_list.20.BatchNorm2d.running_mean, %module_list.20.BatchNorm2d.running_var)
%497 = LeakyRelualpha = 0.100000001490116
%498 = Add(%497, %491)
%499 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%498, %module_list.22.Conv2d.weight)
%500 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%499, %module_list.22.BatchNorm2d.weight, %module_list.22.BatchNorm2d.bias, %module_list.22.BatchNorm2d.running_mean, %module_list.22.BatchNorm2d.running_var)
%501 = LeakyRelualpha = 0.100000001490116
%502 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%501, %module_list.23.Conv2d.weight)
%503 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%502, %module_list.23.BatchNorm2d.weight, %module_list.23.BatchNorm2d.bias, %module_list.23.BatchNorm2d.running_mean, %module_list.23.BatchNorm2d.running_var)
%504 = LeakyRelualpha = 0.100000001490116
%505 = Add(%504, %498)
%506 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%505, %module_list.25.Conv2d.weight)
%507 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%506, %module_list.25.BatchNorm2d.weight, %module_list.25.BatchNorm2d.bias, %module_list.25.BatchNorm2d.running_mean, %module_list.25.BatchNorm2d.running_var)
%508 = LeakyRelualpha = 0.100000001490116
%509 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%508, %module_list.26.Conv2d.weight)
%510 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%509, %module_list.26.BatchNorm2d.weight, %module_list.26.BatchNorm2d.bias, %module_list.26.BatchNorm2d.running_mean, %module_list.26.BatchNorm2d.running_var)
%511 = LeakyRelualpha = 0.100000001490116
%512 = Add(%511, %505)
%513 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%512, %module_list.28.Conv2d.weight)
%514 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%513, %module_list.28.BatchNorm2d.weight, %module_list.28.BatchNorm2d.bias, %module_list.28.BatchNorm2d.running_mean, %module_list.28.BatchNorm2d.running_var)
%515 = LeakyRelualpha = 0.100000001490116
%516 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%515, %module_list.29.Conv2d.weight)
%517 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%516, %module_list.29.BatchNorm2d.weight, %module_list.29.BatchNorm2d.bias, %module_list.29.BatchNorm2d.running_mean, %module_list.29.BatchNorm2d.running_var)
%518 = LeakyRelualpha = 0.100000001490116
%519 = Add(%518, %512)
%520 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%519, %module_list.31.Conv2d.weight)
%521 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%520, %module_list.31.BatchNorm2d.weight, %module_list.31.BatchNorm2d.bias, %module_list.31.BatchNorm2d.running_mean, %module_list.31.BatchNorm2d.running_var)
%522 = LeakyRelualpha = 0.100000001490116
%523 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%522, %module_list.32.Conv2d.weight)
%524 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%523, %module_list.32.BatchNorm2d.weight, %module_list.32.BatchNorm2d.bias, %module_list.32.BatchNorm2d.running_mean, %module_list.32.BatchNorm2d.running_var)
%525 = LeakyRelualpha = 0.100000001490116
%526 = Add(%525, %519)
%527 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%526, %module_list.34.Conv2d.weight)
%528 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%527, %module_list.34.BatchNorm2d.weight, %module_list.34.BatchNorm2d.bias, %module_list.34.BatchNorm2d.running_mean, %module_list.34.BatchNorm2d.running_var)
%529 = LeakyRelualpha = 0.100000001490116
%530 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%529, %module_list.35.Conv2d.weight)
%531 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%530, %module_list.35.BatchNorm2d.weight, %module_list.35.BatchNorm2d.bias, %module_list.35.BatchNorm2d.running_mean, %module_list.35.BatchNorm2d.running_var)
%532 = LeakyRelualpha = 0.100000001490116
%533 = Add(%532, %526)
%534 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%533, %module_list.37.Conv2d.weight)
%535 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%534, %module_list.37.BatchNorm2d.weight, %module_list.37.BatchNorm2d.bias, %module_list.37.BatchNorm2d.running_mean, %module_list.37.BatchNorm2d.running_var)
%536 = LeakyRelualpha = 0.100000001490116
%537 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%536, %module_list.38.Conv2d.weight)
%538 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%537, %module_list.38.BatchNorm2d.weight, %module_list.38.BatchNorm2d.bias, %module_list.38.BatchNorm2d.running_mean, %module_list.38.BatchNorm2d.running_var)
%539 = LeakyRelualpha = 0.100000001490116
%540 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%539, %module_list.39.Conv2d.weight)
%541 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%540, %module_list.39.BatchNorm2d.weight, %module_list.39.BatchNorm2d.bias, %module_list.39.BatchNorm2d.running_mean, %module_list.39.BatchNorm2d.running_var)
%542 = LeakyRelualpha = 0.100000001490116
%543 = Add(%542, %536)
%544 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%543, %module_list.41.Conv2d.weight)
%545 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%544, %module_list.41.BatchNorm2d.weight, %module_list.41.BatchNorm2d.bias, %module_list.41.BatchNorm2d.running_mean, %module_list.41.BatchNorm2d.running_var)
%546 = LeakyRelualpha = 0.100000001490116
%547 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%546, %module_list.42.Conv2d.weight)
%548 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%547, %module_list.42.BatchNorm2d.weight, %module_list.42.BatchNorm2d.bias, %module_list.42.BatchNorm2d.running_mean, %module_list.42.BatchNorm2d.running_var)
%549 = LeakyRelualpha = 0.100000001490116
%550 = Add(%549, %543)
%551 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%550, %module_list.44.Conv2d.weight)
%552 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%551, %module_list.44.BatchNorm2d.weight, %module_list.44.BatchNorm2d.bias, %module_list.44.BatchNorm2d.running_mean, %module_list.44.BatchNorm2d.running_var)
%553 = LeakyRelualpha = 0.100000001490116
%554 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%553, %module_list.45.Conv2d.weight)
%555 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%554, %module_list.45.BatchNorm2d.weight, %module_list.45.BatchNorm2d.bias, %module_list.45.BatchNorm2d.running_mean, %module_list.45.BatchNorm2d.running_var)
%556 = LeakyRelualpha = 0.100000001490116
%557 = Add(%556, %550)
%558 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%557, %module_list.47.Conv2d.weight)
%559 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%558, %module_list.47.BatchNorm2d.weight, %module_list.47.BatchNorm2d.bias, %module_list.47.BatchNorm2d.running_mean, %module_list.47.BatchNorm2d.running_var)
%560 = LeakyRelualpha = 0.100000001490116
%561 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%560, %module_list.48.Conv2d.weight)
%562 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%561, %module_list.48.BatchNorm2d.weight, %module_list.48.BatchNorm2d.bias, %module_list.48.BatchNorm2d.running_mean, %module_list.48.BatchNorm2d.running_var)
%563 = LeakyRelualpha = 0.100000001490116
%564 = Add(%563, %557)
%565 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%564, %module_list.50.Conv2d.weight)
%566 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%565, %module_list.50.BatchNorm2d.weight, %module_list.50.BatchNorm2d.bias, %module_list.50.BatchNorm2d.running_mean, %module_list.50.BatchNorm2d.running_var)
%567 = LeakyRelualpha = 0.100000001490116
%568 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%567, %module_list.51.Conv2d.weight)
%569 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%568, %module_list.51.BatchNorm2d.weight, %module_list.51.BatchNorm2d.bias, %module_list.51.BatchNorm2d.running_mean, %module_list.51.BatchNorm2d.running_var)
%570 = LeakyRelualpha = 0.100000001490116
%571 = Add(%570, %564)
%572 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%571, %module_list.53.Conv2d.weight)
%573 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%572, %module_list.53.BatchNorm2d.weight, %module_list.53.BatchNorm2d.bias, %module_list.53.BatchNorm2d.running_mean, %module_list.53.BatchNorm2d.running_var)
%574 = LeakyRelualpha = 0.100000001490116
%575 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%574, %module_list.54.Conv2d.weight)
%576 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%575, %module_list.54.BatchNorm2d.weight, %module_list.54.BatchNorm2d.bias, %module_list.54.BatchNorm2d.running_mean, %module_list.54.BatchNorm2d.running_var)
%577 = LeakyRelualpha = 0.100000001490116
%578 = Add(%577, %571)
%579 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%578, %module_list.56.Conv2d.weight)
%580 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%579, %module_list.56.BatchNorm2d.weight, %module_list.56.BatchNorm2d.bias, %module_list.56.BatchNorm2d.running_mean, %module_list.56.BatchNorm2d.running_var)
%581 = LeakyRelualpha = 0.100000001490116
%582 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%581, %module_list.57.Conv2d.weight)
%583 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%582, %module_list.57.BatchNorm2d.weight, %module_list.57.BatchNorm2d.bias, %module_list.57.BatchNorm2d.running_mean, %module_list.57.BatchNorm2d.running_var)
%584 = LeakyRelualpha = 0.100000001490116
%585 = Add(%584, %578)
%586 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%585, %module_list.59.Conv2d.weight)
%587 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%586, %module_list.59.BatchNorm2d.weight, %module_list.59.BatchNorm2d.bias, %module_list.59.BatchNorm2d.running_mean, %module_list.59.BatchNorm2d.running_var)
%588 = LeakyRelualpha = 0.100000001490116
%589 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%588, %module_list.60.Conv2d.weight)
%590 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%589, %module_list.60.BatchNorm2d.weight, %module_list.60.BatchNorm2d.bias, %module_list.60.BatchNorm2d.running_mean, %module_list.60.BatchNorm2d.running_var)
%591 = LeakyRelualpha = 0.100000001490116
%592 = Add(%591, %585)
%593 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%592, %module_list.62.Conv2d.weight)
%594 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%593, %module_list.62.BatchNorm2d.weight, %module_list.62.BatchNorm2d.bias, %module_list.62.BatchNorm2d.running_mean, %module_list.62.BatchNorm2d.running_var)
%595 = LeakyRelualpha = 0.100000001490116
%596 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%595, %module_list.63.Conv2d.weight)
%597 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%596, %module_list.63.BatchNorm2d.weight, %module_list.63.BatchNorm2d.bias, %module_list.63.BatchNorm2d.running_mean, %module_list.63.BatchNorm2d.running_var)
%598 = LeakyRelualpha = 0.100000001490116
%599 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%598, %module_list.64.Conv2d.weight)
%600 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%599, %module_list.64.BatchNorm2d.weight, %module_list.64.BatchNorm2d.bias, %module_list.64.BatchNorm2d.running_mean, %module_list.64.BatchNorm2d.running_var)
%601 = LeakyRelualpha = 0.100000001490116
%602 = Add(%601, %595)
%603 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%602, %module_list.66.Conv2d.weight)
%604 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%603, %module_list.66.BatchNorm2d.weight, %module_list.66.BatchNorm2d.bias, %module_list.66.BatchNorm2d.running_mean, %module_list.66.BatchNorm2d.running_var)
%605 = LeakyRelualpha = 0.100000001490116
%606 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%605, %module_list.67.Conv2d.weight)
%607 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%606, %module_list.67.BatchNorm2d.weight, %module_list.67.BatchNorm2d.bias, %module_list.67.BatchNorm2d.running_mean, %module_list.67.BatchNorm2d.running_var)
%608 = LeakyRelualpha = 0.100000001490116
%609 = Add(%608, %602)
%610 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%609, %module_list.69.Conv2d.weight)
%611 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%610, %module_list.69.BatchNorm2d.weight, %module_list.69.BatchNorm2d.bias, %module_list.69.BatchNorm2d.running_mean, %module_list.69.BatchNorm2d.running_var)
%612 = LeakyRelualpha = 0.100000001490116
%613 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%612, %module_list.70.Conv2d.weight)
%614 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%613, %module_list.70.BatchNorm2d.weight, %module_list.70.BatchNorm2d.bias, %module_list.70.BatchNorm2d.running_mean, %module_list.70.BatchNorm2d.running_var)
%615 = LeakyRelualpha = 0.100000001490116
%616 = Add(%615, %609)
%617 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%616, %module_list.72.Conv2d.weight)
%618 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%617, %module_list.72.BatchNorm2d.weight, %module_list.72.BatchNorm2d.bias, %module_list.72.BatchNorm2d.running_mean, %module_list.72.BatchNorm2d.running_var)
%619 = LeakyRelualpha = 0.100000001490116
%620 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%619, %module_list.73.Conv2d.weight)
%621 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%620, %module_list.73.BatchNorm2d.weight, %module_list.73.BatchNorm2d.bias, %module_list.73.BatchNorm2d.running_mean, %module_list.73.BatchNorm2d.running_var)
%622 = LeakyRelualpha = 0.100000001490116
%623 = Add(%622, %616)
%624 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%623, %module_list.75.Conv2d.weight)
%625 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%624, %module_list.75.BatchNorm2d.weight, %module_list.75.BatchNorm2d.bias, %module_list.75.BatchNorm2d.running_mean, %module_list.75.BatchNorm2d.running_var)
%626 = LeakyRelualpha = 0.100000001490116
%627 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%626, %module_list.76.Conv2d.weight)
%628 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%627, %module_list.76.BatchNorm2d.weight, %module_list.76.BatchNorm2d.bias, %module_list.76.BatchNorm2d.running_mean, %module_list.76.BatchNorm2d.running_var)
%629 = LeakyRelualpha = 0.100000001490116
%630 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%629, %module_list.77.Conv2d.weight)
%631 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%630, %module_list.77.BatchNorm2d.weight, %module_list.77.BatchNorm2d.bias, %module_list.77.BatchNorm2d.running_mean, %module_list.77.BatchNorm2d.running_var)
%632 = LeakyRelualpha = 0.100000001490116
%633 = MaxPoolceil_mode = 0, kernel_shape = [5, 5], pads = [2, 2, 2, 2], strides = [1, 1]
%634 = MaxPoolceil_mode = 0, kernel_shape = [9, 9], pads = [4, 4, 4, 4], strides = [1, 1]
%635 = MaxPoolceil_mode = 0, kernel_shape = [13, 13], pads = [6, 6, 6, 6], strides = [1, 1]
%636 = Concat[axis = 1](%635, %634, %633, %632)
%637 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%636, %module_list.84.Conv2d.weight)
%638 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%637, %module_list.84.BatchNorm2d.weight, %module_list.84.BatchNorm2d.bias, %module_list.84.BatchNorm2d.running_mean, %module_list.84.BatchNorm2d.running_var)
%639 = LeakyRelualpha = 0.100000001490116
%640 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%639, %module_list.85.Conv2d.weight)
%641 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%640, %module_list.85.BatchNorm2d.weight, %module_list.85.BatchNorm2d.bias, %module_list.85.BatchNorm2d.running_mean, %module_list.85.BatchNorm2d.running_var)
%642 = LeakyRelualpha = 0.100000001490116
%643 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%642, %module_list.86.Conv2d.weight)
%644 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%643, %module_list.86.BatchNorm2d.weight, %module_list.86.BatchNorm2d.bias, %module_list.86.BatchNorm2d.running_mean, %module_list.86.BatchNorm2d.running_var)
%645 = LeakyRelualpha = 0.100000001490116
%646 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%645, %module_list.87.Conv2d.weight)
%647 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%646, %module_list.87.BatchNorm2d.weight, %module_list.87.BatchNorm2d.bias, %module_list.87.BatchNorm2d.running_mean, %module_list.87.BatchNorm2d.running_var)
%648 = LeakyRelualpha = 0.100000001490116
%649 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%648, %module_list.88.Conv2d.weight, %module_list.88.Conv2d.bias)
%650 = Constantvalue =
%651 = Reshape(%649, %650)
%652 = Transposeperm = [0, 1, 3, 4, 2]
%653 = Constantvalue =
%654 = Reshape(%652, %653)
%655 = Constantvalue =
%656 = Constantvalue =
%657 = Constantvalue =
%658 = Constantvalue =
%659 = Slice(%654, %656, %657, %655, %658)
%660 = Sigmoid(%659)
%661 = Constantvalue =
%662 = Add(%660, %661)
%663 = Constantvalue =
%664 = Constantvalue =
%665 = Constantvalue =
%666 = Constantvalue =
%667 = Slice(%654, %664, %665, %663, %666)
%668 = Exp(%667)
%669 = Constantvalue =
%670 = Mul(%668, %669)
%671 = Constantvalue =
%672 = Constantvalue =
%673 = Constantvalue =
%674 = Constantvalue =
%675 = Slice(%654, %672, %673, %671, %674)
%676 = Sigmoid(%675)
%677 = Constantvalue =
%678 = Constantvalue =
%679 = Constantvalue =
%680 = Constantvalue =
%681 = Slice(%654, %678, %679, %677, %680)
%682 = Sigmoid(%681)
%683 = Mul(%676, %682)
%684 = Constantvalue =
%685 = Div(%662, %684)
%686 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%645, %module_list.91.Conv2d.weight)
%687 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%686, %module_list.91.BatchNorm2d.weight, %module_list.91.BatchNorm2d.bias, %module_list.91.BatchNorm2d.running_mean, %module_list.91.BatchNorm2d.running_var)
%688 = LeakyRelualpha = 0.100000001490116
%689 = Shape(%688)
%690 = Constantvalue = <Scalar Tensor []>
%691 = Gather[axis = 0](%689, %690)
%692 = Castto = 1
%693 = Constantvalue = <Scalar Tensor []>
%694 = Mul(%692, %693)
%695 = Castto = 1
%696 = Floor(%695)
%697 = Shape(%688)
%698 = Constantvalue = <Scalar Tensor []>
%699 = Gather[axis = 0](%697, %698)
%700 = Castto = 1
%701 = Constantvalue = <Scalar Tensor []>
%702 = Mul(%700, %701)
%703 = Castto = 1
%704 = Floor(%703)
%705 = Unsqueezeaxes = [0]
%706 = Unsqueezeaxes = [0]
%707 = Concat[axis = 0](%705, %706)
%708 = Constantvalue =
%709 = Shape(%688)
%710 = Constantvalue =
%711 = Constantvalue =
%712 = Constantvalue =
%713 = Slice(%709, %711, %712, %710)
%714 = Castto = 7
%715 = Concat[axis = 0](%713, %714)
%716 = Resize[coordinate_transformation_mode = ‘asymmetric’, cubic_coeff_a = -0.75, mode = ‘nearest’, nearest_mode = ‘floor’](%688, %708, %708, %715)
%717 = Concat[axis = 1](%716, %592)
%718 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%717, %module_list.94.Conv2d.weight)
%719 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%718, %module_list.94.BatchNorm2d.weight, %module_list.94.BatchNorm2d.bias, %module_list.94.BatchNorm2d.running_mean, %module_list.94.BatchNorm2d.running_var)
%720 = LeakyRelualpha = 0.100000001490116
%721 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%720, %module_list.95.Conv2d.weight)
%722 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%721, %module_list.95.BatchNorm2d.weight, %module_list.95.BatchNorm2d.bias, %module_list.95.BatchNorm2d.running_mean, %module_list.95.BatchNorm2d.running_var)
%723 = LeakyRelualpha = 0.100000001490116
%724 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%723, %module_list.96.Conv2d.weight)
%725 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%724, %module_list.96.BatchNorm2d.weight, %module_list.96.BatchNorm2d.bias, %module_list.96.BatchNorm2d.running_mean, %module_list.96.BatchNorm2d.running_var)
%726 = LeakyRelualpha = 0.100000001490116
%727 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%726, %module_list.97.Conv2d.weight)
%728 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%727, %module_list.97.BatchNorm2d.weight, %module_list.97.BatchNorm2d.bias, %module_list.97.BatchNorm2d.running_mean, %module_list.97.BatchNorm2d.running_var)
%729 = LeakyRelualpha = 0.100000001490116
%730 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%729, %module_list.98.Conv2d.weight)
%731 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%730, %module_list.98.BatchNorm2d.weight, %module_list.98.BatchNorm2d.bias, %module_list.98.BatchNorm2d.running_mean, %module_list.98.BatchNorm2d.running_var)
%732 = LeakyRelualpha = 0.100000001490116
%733 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%732, %module_list.99.Conv2d.weight)
%734 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%733, %module_list.99.BatchNorm2d.weight, %module_list.99.BatchNorm2d.bias, %module_list.99.BatchNorm2d.running_mean, %module_list.99.BatchNorm2d.running_var)
%735 = LeakyRelualpha = 0.100000001490116
%736 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%735, %module_list.100.Conv2d.weight, %module_list.100.Conv2d.bias)
%737 = Constantvalue =
%738 = Reshape(%736, %737)
%739 = Transposeperm = [0, 1, 3, 4, 2]
%740 = Constantvalue =
%741 = Reshape(%739, %740)
%742 = Constantvalue =
%743 = Constantvalue =
%744 = Constantvalue =
%745 = Constantvalue =
%746 = Slice(%741, %743, %744, %742, %745)
%747 = Sigmoid(%746)
%748 = Constantvalue =
%749 = Add(%747, %748)
%750 = Constantvalue =
%751 = Constantvalue =
%752 = Constantvalue =
%753 = Constantvalue =
%754 = Slice(%741, %751, %752, %750, %753)
%755 = Exp(%754)
%756 = Constantvalue =
%757 = Mul(%755, %756)
%758 = Constantvalue =
%759 = Constantvalue =
%760 = Constantvalue =
%761 = Constantvalue =
%762 = Slice(%741, %759, %760, %758, %761)
%763 = Sigmoid(%762)
%764 = Constantvalue =
%765 = Constantvalue =
%766 = Constantvalue =
%767 = Constantvalue =
%768 = Slice(%741, %765, %766, %764, %767)
%769 = Sigmoid(%768)
%770 = Mul(%763, %769)
%771 = Constantvalue =
%772 = Div(%749, %771)
%773 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%732, %module_list.103.Conv2d.weight)
%774 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%773, %module_list.103.BatchNorm2d.weight, %module_list.103.BatchNorm2d.bias, %module_list.103.BatchNorm2d.running_mean, %module_list.103.BatchNorm2d.running_var)
%775 = LeakyRelualpha = 0.100000001490116
%776 = Shape(%775)
%777 = Constantvalue = <Scalar Tensor []>
%778 = Gather[axis = 0](%776, %777)
%779 = Castto = 1
%780 = Constantvalue = <Scalar Tensor []>
%781 = Mul(%779, %780)
%782 = Castto = 1
%783 = Floor(%782)
%784 = Shape(%775)
%785 = Constantvalue = <Scalar Tensor []>
%786 = Gather[axis = 0](%784, %785)
%787 = Castto = 1
%788 = Constantvalue = <Scalar Tensor []>
%789 = Mul(%787, %788)
%790 = Castto = 1
%791 = Floor(%790)
%792 = Unsqueezeaxes = [0]
%793 = Unsqueezeaxes = [0]
%794 = Concat[axis = 0](%792, %793)
%795 = Constantvalue =
%796 = Shape(%775)
%797 = Constantvalue =
%798 = Constantvalue =
%799 = Constantvalue =
%800 = Slice(%796, %798, %799, %797)
%801 = Castto = 7
%802 = Concat[axis = 0](%800, %801)
%803 = Resize[coordinate_transformation_mode = ‘asymmetric’, cubic_coeff_a = -0.75, mode = ‘nearest’, nearest_mode = ‘floor’](%775, %795, %795, %802)
%804 = Concat[axis = 1](%803, %533)
%805 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%804, %module_list.106.Conv2d.weight)
%806 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%805, %module_list.106.BatchNorm2d.weight, %module_list.106.BatchNorm2d.bias, %module_list.106.BatchNorm2d.running_mean, %module_list.106.BatchNorm2d.running_var)
%807 = LeakyRelualpha = 0.100000001490116
%808 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%807, %module_list.107.Conv2d.weight)
%809 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%808, %module_list.107.BatchNorm2d.weight, %module_list.107.BatchNorm2d.bias, %module_list.107.BatchNorm2d.running_mean, %module_list.107.BatchNorm2d.running_var)
%810 = LeakyRelualpha = 0.100000001490116
%811 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%810, %module_list.108.Conv2d.weight)
%812 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%811, %module_list.108.BatchNorm2d.weight, %module_list.108.BatchNorm2d.bias, %module_list.108.BatchNorm2d.running_mean, %module_list.108.BatchNorm2d.running_var)
%813 = LeakyRelualpha = 0.100000001490116
%814 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%813, %module_list.109.Conv2d.weight)
%815 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%814, %module_list.109.BatchNorm2d.weight, %module_list.109.BatchNorm2d.bias, %module_list.109.BatchNorm2d.running_mean, %module_list.109.BatchNorm2d.running_var)
%816 = LeakyRelualpha = 0.100000001490116
%817 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%816, %module_list.110.Conv2d.weight)
%818 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%817, %module_list.110.BatchNorm2d.weight, %module_list.110.BatchNorm2d.bias, %module_list.110.BatchNorm2d.running_mean, %module_list.110.BatchNorm2d.running_var)
%819 = LeakyRelualpha = 0.100000001490116
%820 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%819, %module_list.111.Conv2d.weight)
%821 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%820, %module_list.111.BatchNorm2d.weight, %module_list.111.BatchNorm2d.bias, %module_list.111.BatchNorm2d.running_mean, %module_list.111.BatchNorm2d.running_var)
%822 = LeakyRelualpha = 0.100000001490116
%823 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%822, %module_list.112.Conv2d.weight, %module_list.112.Conv2d.bias)
%824 = Constantvalue =
%825 = Reshape(%823, %824)
%826 = Transposeperm = [0, 1, 3, 4, 2]
%827 = Constantvalue =
%828 = Reshape(%826, %827)
%829 = Constantvalue =
%830 = Constantvalue =
%831 = Constantvalue =
%832 = Constantvalue =
%833 = Slice(%828, %830, %831, %829, %832)
%834 = Sigmoid(%833)
%835 = Constantvalue =
%836 = Add(%834, %835)
%837 = Constantvalue =
%838 = Constantvalue =
%839 = Constantvalue =
%840 = Constantvalue =
%841 = Slice(%828, %838, %839, %837, %840)
%842 = Exp(%841)
%843 = Constantvalue =
%844 = Mul(%842, %843)
%845 = Constantvalue =
%846 = Constantvalue =
%847 = Constantvalue =
%848 = Constantvalue =
%849 = Slice(%828, %846, %847, %845, %848)
%850 = Sigmoid(%849)
%851 = Constantvalue =
%852 = Constantvalue =
%853 = Constantvalue =
%854 = Constantvalue =
%855 = Slice(%828, %852, %853, %851, %854)
%856 = Sigmoid(%855)
%857 = Mul(%850, %856)
%858 = Constantvalue =
%859 = Div(%836, %858)
%860 = Concat[axis = 0](%683, %770, %857)
%861 = Concat[axis = 0](%685, %772, %859)
%862 = Concat[axis = 0](%670, %757, %844)
%863 = Concat[axis = 1](%861, %862)
return %860, %863
}