code: def forward(self, inputs: Tensor) -> Tensor:
      if isinstance(inputs, Tensor):
          prev_features = [inputs]
      else:
          prev_features = inputs
  
      if self.memory_efficient and self.any_requires_grad(
          prev_features
      ):
          if torch.jit.is_scripting():
              raise Exception("memory efficient not supported in JIT")
  
          bottleneck_output = self.call_checkpoint_bottleneck(
              prev_features
          )
      else:
          bottleneck_output = self.bn_function(prev_features)
  
      new_features = self.conv2(
          self.relu2(self.norm2(bottleneck_output))
      )
      if self.drop_rate > 0:
          new_features = F.dropout(
              new_features, p=self.drop_rate, training=self.training
          )
  
      return new_features
  
imports: import re
  from typing import Any, List, Tuple
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import Tensor
label_desc: Use image node_id 5 for calling the _DenseLayer obj's underlying member method code with example usage: obj.forward(inputs) and returns: new_features