class_docstr: DeeplabV2 Resnet implementation with ASPP.
code: def __init__(
      self, num_classes, small=True, pretrained=False, **kwargs
  ):
      super(DeepLabv2_ASPP, self).__init__()
      block = Atrous_Bottleneck
      self.resnet_features = Atrous_ResNet_features(
          block, [3, 4, 23, 3], pretrained
      )
  
      if small:
          rates = [2, 4, 8, 12]
      else:
          rates = [6, 12, 18, 24]
      self.aspp1 = Atrous_module(2048, num_classes, rate=rates[0])
      self.aspp2 = Atrous_module(2048, num_classes, rate=rates[1])
      self.aspp3 = Atrous_module(2048, num_classes, rate=rates[2])
      self.aspp4 = Atrous_module(2048, num_classes, rate=rates[3])
  
imports: import math
  import torch.nn
  import torch.nn.functional
  import torchvision.models
label_desc: Use image node_id 1 to create a new DeepLabv2_ASPP object from inherited base classes: nn with example: obj = DeepLabv2_ASPP(num_classes, small, pretrained)