code: def check_forward(self, x0_data, x1_data, t_data):
      x0_val = chainer.Variable(x0_data)
      x1_val = chainer.Variable(x1_data)
      t_val = chainer.Variable(t_data)
      loss = functions.contrastive(
          x0_val, x1_val, t_val, self.margin, self.reduce
      )
      self.assertEqual(loss.data.dtype, self.dtype)
      if self.reduce == "mean":
          self.assertEqual(loss.data.shape, ())
      else:
          self.assertEqual(loss.data.shape, (self.batchsize,))
      loss_value = cuda.to_cpu(loss.data)
  
      # Compute expected value
      loss_expect = numpy.empty((self.batchsize,), self.dtype)
      for i in six.moves.range(self.x0.shape[0]):
          x0d, x1d, td = self.x0[i], self.x1[i], self.t[i]
          d = numpy.sum((x0d - x1d) ** 2)
          if td == 1:  # similar pair
              loss_expect[i] = d
          elif td == 0:  # dissimilar pair
              loss_expect[i] = max(self.margin - math.sqrt(d), 0) ** 2
          loss_expect[i] /= 2.0
      if self.reduce == "mean":
          loss_expect = numpy.sum(loss_expect) / self.t.shape[0]
      numpy.testing.assert_allclose(
          loss_expect, loss_value, **self.forward_options
      )
  
imports: import math
  import unittest
  import numpy
  import six
  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 2 for calling the TestContrastive obj's underlying member method code with example usage: obj.check_forward(x0_data, x1_data, t_data) without return types