code: def check_forward(self, a_data, p_data, n_data):
      a_val = chainer.Variable(a_data)
      p_val = chainer.Variable(p_data)
      n_val = chainer.Variable(n_data)
      loss = functions.triplet(
          a_val, p_val, n_val, self.margin, self.reduce
      )
      if self.reduce == "mean":
          self.assertEqual(loss.data.shape, ())
      else:
          self.assertEqual(loss.data.shape, (self.batchsize,))
      self.assertEqual(loss.data.dtype, self.dtype)
      loss_value = cuda.to_cpu(loss.data)
  
      #
      # Compute expected value
      #
      loss_expect = numpy.empty((self.a.shape[0],), dtype=self.dtype)
      for i in six.moves.range(self.a.shape[0]):
          ad, pd, nd = self.a[i], self.p[i], self.n[i]
          dp = numpy.sum((ad - pd) ** 2)
          dn = numpy.sum((ad - nd) ** 2)
          loss_expect[i] = max((dp - dn + self.margin), 0)
      if self.reduce == "mean":
          loss_expect = loss_expect.mean()
      numpy.testing.assert_allclose(
          loss_expect, loss_value, **self.check_forward_options
      )
  
imports: 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 TestTriplet obj's underlying member method code with example usage: obj.check_forward(a_data, p_data, n_data) without return types