code: def __init__(self, num_classes, pretrained=True, k=7, **kwargs):
      super(GCN_Densenet, self).__init__()
  
      self.K = k
  
      densenet = models.densenet161(pretrained=pretrained)
  
      self.layer0 = nn.Sequential(
          densenet.features.conv0,
          densenet.features.norm0,
          densenet.features.relu0,
      )
  
      self.layer1 = nn.Sequential(
          densenet.features.pool0,
          densenet.features.denseblock1,
      )
  
      self.layer2 = nn.Sequential(
          densenet.features.transition1,
          densenet.features.denseblock2,
      )
  
      self.layer3 = nn.Sequential(
          densenet.features.transition2,
          densenet.features.denseblock3,
      )
  
      self.layer4 = nn.Sequential(
          densenet.features.transition3,
          densenet.features.denseblock4,
      )
  
      self.gcm1 = _GlobalConvModule(2208, num_classes, (self.K, self.K))
      self.gcm2 = _GlobalConvModule(2112, num_classes, (self.K, self.K))
      self.gcm3 = _GlobalConvModule(768, num_classes, (self.K, self.K))
      self.gcm4 = _GlobalConvModule(384, num_classes, (self.K, self.K))
  
      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)
  
      initialize_weights(
          self.gcm1,
          self.gcm2,
          self.gcm3,
          self.gcm4,
          self.brm1,
          self.brm2,
          self.brm3,
          self.brm4,
          self.brm5,
          self.brm6,
          self.brm7,
          self.brm8,
          self.brm9,
      )
  
file_path: pywick/pywick/models/segmentation/gcnnets/gcn_densenet.py
imports: from math import floor
  import torch.nn.functional
  from torch import nn
  from torchvision import models
label_desc: Use image node_id 1 to create a new GCN_Densenet object from inherited base classes: nn with example: obj = GCN_Densenet(num_classes, pretrained, k)