code: def forward(self, xs, ys):
      # h[i] is feature vector for each batch of words.
      hs = [self.feature(x) for x in xs]
      loss = self.crf(hs, ys, transpose=True)
      reporter.report({"loss": loss}, self)
  
      # To predict labels, call argmax method.
      _, predict = self.crf.argmax(hs, transpose=True)
      correct = 0
      total = 0
      for y, p in six.moves.zip(ys, predict):
          # NOTE y is ndarray because
          # it does not pass to transpose_sequence
          correct += self.xp.sum(y == p)
          total += len(y)
      reporter.report({"correct": correct}, self)
      reporter.report({"total": total}, self)
  
      return loss
  
file_path: chainer/examples/pos/postagging.py
imports: import argparse
  import collections
  import warnings
  import nltk
  import numpy
  import six
  import chainer
  from chainer import datasets
  import chainer.links
  from chainer import reporter
  from chainer import training
  from chainer.training import extensions
label_desc: Use image node_id 2 for calling the CRF obj's underlying member method code with example usage: obj.forward(xs, ys) and returns: loss