code: def forward(self, x):
      out = self.start_features(x)
      deep_features = None
      for i, block in enumerate(self.blocks):
          out = block(out)
          if i == 5:
              deep_features = out
  
      return out, deep_features
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
label_desc: Use image node_id 2 for calling the DenseNet obj's underlying member method code with example usage: obj.forward(x) and returns: out,  deep_features