code: def _densenet(
      arch,
      growth_rate,
      block_config,
      num_init_features,
      pretrained,
      progress,
      **kwargs,
  ):
      model = DenseNet(
          growth_rate, block_config, num_init_features, **kwargs
      )
      if pretrained:
          _load_state_dict(model, model_urls[arch], progress)
      return model
  
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 a global function with example usage: _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress) and returns: model