code: def forward(self, x):
      new_features = super(_DenseLayer, self).forward(x)
      if self.drop_rate > 0:
          new_features = F.dropout(
              new_features, p=self.drop_rate, training=self.training
          )
      return torch.cat([x, new_features], 1)
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from antialiased_cnns import *
label_desc: Use image node_id 2 for calling the _DenseLayer obj's underlying member method code with example usage: obj.forward(x) and returns: torch