code: def get_dilated_densenet(
      num_layers, dilate_scale, pretrained=False, **kwargs
  ):
      num_init_features, growth_rate, block_config = densenet_spec[
          num_layers
      ]
      model = DilatedDenseNet(
          growth_rate,
          block_config,
          num_init_features,
          dilate_scale=dilate_scale,
      )
      if pretrained:
          # '.'s are no longer allowed in module names, but pervious _DenseLayer
          # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
          # They are also in the checkpoints in model_urls. This pattern is used
          # to find such keys.
          pattern = re.compile(
              r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
          )
          state_dict = model_zoo.load_url(
              model_urls["densenet%d" % num_layers]
          )
          for key in list(state_dict.keys()):
              res = pattern.match(key)
              if res:
                  new_key = res.group(1) + res.group(2)
                  state_dict[new_key] = state_dict[key]
                  del state_dict[key]
          model.load_state_dict(state_dict)
      return model
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling a global function with example usage: get_dilated_densenet(num_layers, dilate_scale, pretrained) and returns: model