code: def forward(self, imgs, labels):
      h_aux, h_main = self.model.extractor(imgs)
      h_aux = F.dropout(self.aux_conv1(h_aux), ratio=0.1)
      h_aux = self.aux_conv2(h_aux)
      h_aux = F.resize_images(h_aux, imgs.shape[2:])
  
      h_main = self.model.ppm(h_main)
      h_main = F.dropout(self.model.head_conv1(h_main), ratio=0.1)
      h_main = self.model.head_conv2(h_main)
      h_main = F.resize_images(h_main, imgs.shape[2:])
  
      aux_loss = F.softmax_cross_entropy(h_aux, labels)
      main_loss = F.softmax_cross_entropy(h_main, labels)
      loss = 0.4 * aux_loss + main_loss
  
      chainer.reporter.report({"loss": loss}, self)
      return loss
  
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 2 for calling the TrainChain obj's underlying member method code with example usage: obj.forward(imgs, labels) and returns: loss