code: def densenet121(pretrained=False, progress=True, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
      return _densenet(
          "densenet121",
          32,
          (6, 12, 24, 16),
          64,
          pretrained,
          progress,
          **kwargs,
      )
  
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 3 for calling a global function with example usage: densenet121(pretrained, progress) and returns: _densenet