code: def forward(self, init_features):
      features = [init_features]
      for name, layer in self.items():
          new_features = layer(features)
          features.append(new_features)
      return torch.cat(features, 1)
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from collections import OrderedDict
  from torch.hub import load_state_dict_from_url
  from torch import Tensor
  from torch.jit.annotations import List
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 2 for calling the _DenseBlock obj's underlying member method code with example usage: obj.forward(init_features) and returns: torch