code: def __init__(self, model):
      initialW = chainer.initializers.HeNormal()
      super(TrainChain, self).__init__()
      with self.init_scope():
          self.model = model
          self.aux_conv1 = Conv2DBNActiv(
              None, 512, 3, 1, 1, initialW=initialW
          )
          self.aux_conv2 = L.Convolution2D(
              None, model.n_class, 3, 1, 1, False, initialW=initialW
          )
  
imports: import argparse
  import copy
  import multiprocessing
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  from chainer.training.extensions import PolynomialShift
  from chainercv.datasets import ade20k_semantic_segmentation_label_names
  from chainercv.datasets import ADE20KSemanticSegmentationDataset
  from chainercv.datasets import cityscapes_semantic_segmentation_label_names
  from chainercv.datasets import CityscapesSemanticSegmentationDataset
  from chainercv.experimental.links import PSPNetResNet101
  from chainercv.experimental.links import PSPNetResNet50
  from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
  from chainercv.extensions import SemanticSegmentationEvaluator
  from chainercv.links import Conv2DBNActiv
  from chainercv import transforms
  from chainercv.links.model.ssd import GradientScaling
  import PIL
  import chainermn
label_desc: Use image node_id 1 to create a new TrainChain object from inherited base classes: chainer with example: obj = TrainChain(model)