code: def __init__(
      self, num_classes, small=True, pretrained=True, **kwargs
  ):
      super(DeepLabv3_plus, self).__init__()
      block = Atrous_Bottleneck
      self.resnet_features = Atrous_ResNet_features(
          block, [3, 4, 23], pretrained
      )
  
      rates = [1, 6, 12, 18]
      self.aspp1 = Atrous_module(2048, 256, rate=rates[0])
      self.aspp2 = Atrous_module(2048, 256, rate=rates[1])
      self.aspp3 = Atrous_module(2048, 256, rate=rates[2])
      self.aspp4 = Atrous_module(2048, 256, rate=rates[3])
      self.image_pool = nn.Sequential(
          nn.AdaptiveMaxPool2d(1), nn.Conv2d(2048, 256, kernel_size=1)
      )
  
      self.fc1 = nn.Sequential(
          nn.Conv2d(1280, 256, kernel_size=1), nn.BatchNorm2d(256)
      )
  
      self.reduce_conv2 = nn.Sequential(
          nn.Conv2d(256, 48, kernel_size=1), nn.BatchNorm2d(48)
      )
      self.last_conv = nn.Sequential(
          nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1),
          nn.BatchNorm2d(256),
          nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
          nn.BatchNorm2d(256),
          nn.Conv2d(256, num_classes, kernel_size=1, stride=1),
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.models
label_desc: Use image node_id 1 to create a new DeepLabv3_plus object from inherited base classes: nn with example: obj = DeepLabv3_plus(num_classes, small, pretrained)