code: def check_forward(self, x_data):
      x = chainer.Variable(x_data)
      # Make the batch normalization to be the identity function.
      self.l.bn.avg_var[:] = 1
      self.l.bn.avg_mean[:] = 0
      with chainer.using_config("train", False):
          y = self.l(x)
  
      self.assertIsInstance(y, chainer.Variable)
      self.assertIsInstance(y.array, self.l.xp.ndarray)
  
      if self.dilate == 1:
          _x_data = x_data
      elif self.dilate == 2:
          _x_data = x_data[:, :, 1:-1, 1:-1]
      if self.activ == "relu":
          np.testing.assert_almost_equal(
              cuda.to_cpu(y.array),
              np.maximum(cuda.to_cpu(_x_data), 0),
              decimal=4,
          )
      elif self.activ == "add_one":
          np.testing.assert_almost_equal(
              cuda.to_cpu(y.array), cuda.to_cpu(_x_data) + 1, decimal=4
          )
      elif self.activ is None:
          np.testing.assert_almost_equal(
              cuda.to_cpu(y.array), cuda.to_cpu(_x_data), decimal=4
          )
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.functions import relu
  from chainer import testing
  from chainermn import create_communicator
  from chainercv.links import Conv2DBNActiv
  from chainercv.utils.testing import attr
label_desc: Use image node_id 2 for calling the TestConv2DBNActiv obj's underlying member method code with example usage: obj.check_forward(x_data) without return types