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
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.jit.annotations import List
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import BatchNormAct2d, get_norm_act_layer, BlurPool2d, create_classifier
  from ._builder import build_model_with_cfg
  from ._manipulate import MATCH_PREV_GROUP
  from ._registry import register_model, generate_default_cfgs
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