code: def __init__(self, num_classes, kernel_size=7):
      super(GCN, self).__init__()
      self.resnet_features = ModelBuilder().build_encoder("resnet101")
      self.layer0 = nn.Sequential(
          self.resnet_features.conv1,
          self.resnet_features.bn1,
          self.resnet_features.relu1,
          self.resnet_features.conv3,
          self.resnet_features.bn3,
          self.resnet_features.relu3,
      )
      self.layer1 = nn.Sequential(
          self.resnet_features.maxpool, self.resnet_features.layer1
      )
      self.layer2 = self.resnet_features.layer2
      self.layer3 = self.resnet_features.layer3
      self.layer4 = self.resnet_features.layer4
  
      self.gcm1 = _GlobalConvModule(
          2048, num_classes, (kernel_size, kernel_size)
      )
      self.gcm2 = _GlobalConvModule(
          1024, num_classes, (kernel_size, kernel_size)
      )
      self.gcm3 = _GlobalConvModule(
          512, num_classes, (kernel_size, kernel_size)
      )
      self.gcm4 = _GlobalConvModule(
          256, num_classes, (kernel_size, kernel_size)
      )
  
      self.brm1 = _BoundaryRefineModule(num_classes)
      self.brm2 = _BoundaryRefineModule(num_classes)
      self.brm3 = _BoundaryRefineModule(num_classes)
      self.brm4 = _BoundaryRefineModule(num_classes)
      self.brm5 = _BoundaryRefineModule(num_classes)
      self.brm6 = _BoundaryRefineModule(num_classes)
      self.brm7 = _BoundaryRefineModule(num_classes)
      self.brm8 = _BoundaryRefineModule(num_classes)
      self.brm9 = _BoundaryRefineModule(num_classes)
  
      self.deconv1 = nn.ConvTranspose2d(
          num_classes,
          num_classes,
          kernel_size=4,
          stride=2,
          padding=1,
          bias=False,
      )
      self.deconv2 = nn.ConvTranspose2d(
          num_classes,
          num_classes,
          kernel_size=4,
          stride=2,
          padding=1,
          bias=False,
      )
      self.deconv3 = nn.ConvTranspose2d(
          num_classes,
          num_classes,
          kernel_size=4,
          stride=2,
          padding=1,
          bias=False,
      )
      self.deconv4 = nn.ConvTranspose2d(
          num_classes,
          num_classes,
          kernel_size=4,
          stride=2,
          padding=1,
          bias=False,
      )
      self.deconv5 = nn.ConvTranspose2d(
          num_classes,
          num_classes,
          kernel_size=4,
          stride=2,
          padding=1,
          bias=False,
      )
  
file_path: pywick/pywick/models/segmentation/testnets/lg_kernel_exfuse/large_kernel.py
imports: import torch
  from torch import nn
  from .deeplab_resnet import ModelBuilder
label_desc: Use image node_id 1 to create a new GCN object from inherited base classes: nn with example: obj = GCN(num_classes, kernel_size)