code: def check_forward(self, cost_data, xs_data, ys_data):
      cost = chainer.Variable(cost_data)
      xs = [chainer.Variable(x) for x in xs_data]
      ys = [chainer.Variable(y) for y in ys_data]
      actual = functions.crf1d(cost, xs, ys, reduce=self.reduce)
  
      z = numpy.zeros((self.batches[0],), numpy.float32)
      for b, length in enumerate(self.lengths):
          for ys in itertools.product(
              range(self.n_label), repeat=length
          ):
              z[b] += numpy.exp(self._calc_score(b, ys))
  
      score = numpy.zeros((self.batches[0],), numpy.float32)
      for b, length in enumerate(self.lengths):
          ys = [self.ys[i][b] for i in range(length)]
          score[b] = self._calc_score(b, ys)
  
      loss = -(score - numpy.log(z))
      if self.reduce == "mean":
          expect = numpy.sum(loss) / self.batches[0]
      elif self.reduce == "no":
          expect = loss
  
      testing.assert_allclose(actual.data, expect)
  
imports: import itertools
  import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
label_desc: Use image node_id 3 for calling the TestCRF1d obj's underlying member method code with example usage: obj.check_forward(cost_data, xs_data, ys_data) without return types