code: def forward(self, x):
      output_features = []
      for layer in self.encoder_layers:
          output = layer(x)
          output_features.append(output)
  
          if layer == self.layer0:
              # Fist maxpool operator is not a part of layer0 because we want that layer0 output to have stride of 2
              output = self.pool0(output)
          else:
              output = self.avg_pool(output)
  
          x = output
  
      # Return only features that were requested
      return _take(output_features, self._layers)
  
imports: from collections import OrderedDict
  from typing import List
  import torch
  from torch import nn
  from torchvision.models import densenet121, densenet161, densenet169, densenet201, DenseNet
  from .common import EncoderModule, _take, make_n_channel_input
label_desc: Use image node_id 5 for calling the DenseNetEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: _take