code: def forward(self, x):
      # initial convolutional block:
      out = self.initial_block(x)
  
      # evaluate all dense blocks:
      out = self.features(out)
  
      return out
  
imports: import math
  from collections import OrderedDict
  from typing import Any, Dict, Sequence
  import torch
  import torch.nn
  from classy_vision.generic.util import is_pos_int
  from .None import register_model
  from .classy_model import ClassyModel
  from .squeeze_and_excitation_layer import SqueezeAndExcitationLayer
label_desc: Use image node_id 5 for calling the DenseNet obj's underlying member method code with example usage: obj.forward(x) and returns: out