code: def __init__(
      self,
      nInputChannels=3,
      n_classes=21,
      os=16,
      pretrained=False,
      _print=True,
  ):
      if _print:
          print("Constructing DeepLabv3+ model...")
          print("Number of classes: {}".format(n_classes))
          print("Output stride: {}".format(os))
          print("Number of Input Channels: {}".format(nInputChannels))
      super(DeepLabv3_plus, self).__init__()
  
      # Atrous Conv
      self.xception_features = Xception(nInputChannels, os, pretrained)
  
      # ASPP
      if os == 16:
          rates = [1, 6, 12, 18]
      elif os == 8:
          rates = [1, 12, 24, 36]
          raise NotImplementedError
      else:
          raise NotImplementedError
  
      self.aspp1 = ASPP_module_rate0(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.concat_projection_conv1 = nn.Conv2d(1280, 256, 1, bias=False)
      self.concat_projection_bn1 = nn.BatchNorm2d(256)
  
      # adopt [1x1, 48] for channel reduction.
      self.feature_projection_conv1 = nn.Conv2d(256, 48, 1, bias=False)
      self.feature_projection_bn1 = nn.BatchNorm2d(48)
  
      self.decoder = nn.Sequential(
          Decoder_module(304, 256), Decoder_module(256, 256)
      )
      self.semantic = nn.Conv2d(256, n_classes, kernel_size=1, stride=1)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  import torch.utils.model_zoo
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new DeepLabv3_plus object from inherited base classes: nn with example: obj = DeepLabv3_plus(nInputChannels, n_classes, os, pretrained, _print)