code: def check_forward(self, x_data, t_data):
      x = chainer.Variable(x_data)
      t = chainer.Variable(t_data)
      loss = functions.huber_loss(x, t, delta=1, reduce=self.reduce)
      self.assertEqual(loss.data.dtype, self.dtype)
      loss_value = cuda.to_cpu(loss.data)
  
      diff_data = cuda.to_cpu(x_data) - cuda.to_cpu(t_data)
      loss_expect = numpy.zeros(self.shape)
      mask = numpy.abs(diff_data) < 1
      loss_expect[mask] = 0.5 * diff_data[mask] ** 2
      loss_expect[~mask] = numpy.abs(diff_data[~mask]) - 0.5
      if self.reduce == "sum_along_second_axis":
          loss_expect = numpy.sum(loss_expect, axis=1)
      testing.assert_allclose(
          loss_value, loss_expect, **self.forward_options
      )
  
imports: 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
  from chainer import utils
label_desc: Use image node_id 3 for calling the TestHuberLoss obj's underlying member method code with example usage: obj.check_forward(x_data, t_data) without return types