code: def __init__(self, in_dims, num_classes, use_bn=True):
      super(top, self).__init__()
      if use_bn:
          self.lastact = nn.Sequential(
              nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True)
          )
      else:
          self.lastact = nn.ReLU(inplace=True)
      self.global_pooling = nn.AdaptiveAvgPool2d(1)
      self.classifier = nn.Linear(in_dims, num_classes)
  
imports: import torch.nn
label_desc: Use image node_id 1 to create a new top object from inherited base classes: nn with example: obj = top(in_dims, num_classes, use_bn)