code: def check_forward(self, x_data, t_data):
      x_val = chainer.Variable(x_data)
      t_val = chainer.Variable(t_data, requires_grad=False)
      loss = functions.hinge(x_val, t_val, self.norm, self.reduce)
      if self.reduce == "mean":
          self.assertEqual(loss.data.shape, ())
      else:
          self.assertEqual(loss.data.shape, self.x.shape)
      self.assertEqual(loss.data.dtype, self.dtype)
      loss_value = cuda.to_cpu(loss.data)
  
      # Compute expected value
      for i in six.moves.range(self.x.shape[0]):
          self.x[i, self.t[i]] *= -1
      for i in six.moves.range(self.x.shape[0]):
          for j in six.moves.range(self.x.shape[1]):
              self.x[i, j] = max(0, 1.0 + self.x[i, j])
      if self.norm == "L1":
          loss_expect = self.x
      elif self.norm == "L2":
          loss_expect = self.x**2
      if self.reduce == "mean":
          loss_expect = numpy.sum(loss_expect) / self.x.shape[0]
  
      testing.assert_allclose(
          loss_expect, loss_value, **self.forward_options
      )
  
imports: import unittest
  import numpy
  import six
  import chainer
  from chainer import backend
  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 TestHinge obj's underlying member method code with example usage: obj.check_forward(x_data, t_data) without return types