code: def __init__(
      self,
      num_classes=21,
      pretrained=False,
      nInputChannels=3,
      os=16,
      _print=True,
      **kwargs,
  ):
      if _print:
          print("Constructing DeepLabv3+ model...")
          print("Number of classes: {}".format(num_classes))
          print("Output stride: {}".format(os))
          print("Number of Input Channels: {}".format(nInputChannels))
      super(DeepLabv3_plus, self).__init__()
  
      # Atrous Conv
      self.resnet_features = ResNet101(
          nInputChannels, os, pretrained=pretrained
      )
  
      # ASPP
      if os == 16:
          rates = [1, 6, 12, 18]
      elif os == 8:
          rates = [1, 12, 24, 36]
      else:
          raise NotImplementedError
  
      self.aspp1 = ASPP_module(2048, 256, rate=rates[0])
      self.aspp2 = ASPP_module(2048, 256, rate=rates[1])
      self.aspp3 = ASPP_module(2048, 256, rate=rates[2])
      self.aspp4 = ASPP_module(2048, 256, rate=rates[3])
  
      self.relu = nn.ReLU()
  
      self.global_avg_pool = nn.Sequential(
          nn.AdaptiveAvgPool2d((1, 1)),
          nn.Conv2d(2048, 256, 1, stride=1, bias=False),
          nn.BatchNorm2d(256),
          nn.ReLU(),
      )
  
      self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
      self.bn1 = nn.BatchNorm2d(256)
  
      # adopt [1x1, 48] for channel reduction.
      self.conv2 = nn.Conv2d(256, 48, 1, bias=False)
      self.bn2 = nn.BatchNorm2d(48)
  
      self.last_linear = nn.Sequential(
          nn.Conv2d(
              304, 256, kernel_size=3, stride=1, padding=1, bias=False
          ),
          nn.BatchNorm2d(256),
          nn.ReLU(),
          nn.Conv2d(
              256, 256, kernel_size=3, stride=1, padding=1, bias=False
          ),
          nn.BatchNorm2d(256),
          nn.ReLU(),
          nn.Conv2d(256, num_classes, kernel_size=1, stride=1),
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
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, pretrained, nInputChannels, os, _print)