code: def densenet169(
      pretrained=False,
      filter_size=4,
      pool_only=True,
      _force_nonfinetuned=False,
      **kwargs,
  ):
      r"""Densenet-169 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          filter_size (int): [4] Antialiasing filter size
          pool_only (bool): [True] don't antialias the first downsampling operation (which is costly to antialias)
      """
      model = DenseNet(
          num_init_features=64,
          growth_rate=32,
          block_config=(6, 12, 32, 32),
          filter_size=filter_size,
          pool_only=pool_only,
          **kwargs,
      )
      if pretrained:
          if filter_size == 4:
              _load_state_dict(
                  model, model_urls["densenet169_lpf4_finetune"]
              )
          else:
              raise ValueError("No pretrained model available")
      return model
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from antialiased_cnns import *
label_desc: Use image node_id 3 for calling a global function with example usage: densenet169(pretrained, filter_size, pool_only, _force_nonfinetuned) and returns: model