code: def __init__(
      self,
      growth_rate=12,
      block_config=(6, 12, 24, 16),
      num_init_features=64,
      bn_size=4,
      drop_rate=0,
      num_classes=1000,
      dilate_scale=8,
      norm_layer=nn.BatchNorm2d,
      **kwargs,
  ):
      super(DilatedDenseNet, self).__init__(
          growth_rate,
          block_config,
          num_init_features,
          bn_size,
          drop_rate,
          num_classes,
          norm_layer,
      )
      if dilate_scale not in (8, 16):
          raise AssertionError("dilate_scale can only set as 8 or 16")
      from functools import partial
  
      if dilate_scale == 8:
          self.features.denseblock3.apply(
              partial(self._conv_dilate, dilate=2)
          )
          self.features.denseblock4.apply(
              partial(self._conv_dilate, dilate=4)
          )
          del self.features.transition2.pool
          del self.features.transition3.pool
      elif dilate_scale == 16:
          self.features.denseblock4.apply(
              partial(self._conv_dilate, dilate=2)
          )
          del self.features.transition3.pool
  
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 1 to create a new DilatedDenseNet object from inherited base classes: DenseNet with example: obj = DilatedDenseNet(growth_rate, block_config, num_init_features, bn_size, drop_rate, num_classes, dilate_scale, norm_layer)