code: def get_densenet(
      num_layers,
      pretrained=False,
      device=cpu(),
      root=os.path.join(base.data_dir(), "models"),
      **kwargs,
  ):
      r"""Densenet-BC model from the
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper.
  
      Parameters
      ----------
      num_layers : int
          Number of layers for the variant of densenet. Options are 121, 161, 169, 201.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      device : Device, default CPU
          The device in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.
      """
      num_init_features, growth_rate, block_config = densenet_spec[
          num_layers
      ]
      net = DenseNet(
          num_init_features, growth_rate, block_config, **kwargs
      )
      if pretrained:
          from ..model_store import get_model_file
  
          net.load_parameters(
              get_model_file(f"densenet{num_layers}", root=root),
              device=device,
          )
      return net
  
file_path: mxnet/python/mxnet/gluon/model_zoo/vision/densenet.py
imports: import os
  from ....device import cpu
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 4 for calling a global function with example usage: get_densenet(num_layers, pretrained, device, root) and returns: net