code: def check_forward(self, x_data):
      x = chainer.Variable(x_data)
      # Make the batch normalization to be the identity function.
      if self.expansion_size != 1:
          self.l.expand.bn.avg_var[:] = 1
          self.l.expand.bn.avg_mean[:] = 0
      self.l.depthwise.bn.avg_var[:] = 1
      self.l.depthwise.bn.avg_mean[:] = 0
      self.l.project.bn.avg_var[:] = 1
      self.l.project.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)
  
      _x_data = x_data
      if self.expansion_size > self.in_channels:
          np.testing.assert_almost_equal(
              cuda.to_cpu(y.array),
              cuda.to_cpu(_x_data)
              + self.expansion_size
              * np.maximum(np.minimum(cuda.to_cpu(_x_data), 6), 0),
              decimal=4,
          )
      else:
          np.testing.assert_almost_equal(
              cuda.to_cpu(y.array),
              cuda.to_cpu(_x_data)
              + np.maximum(np.minimum(cuda.to_cpu(_x_data), 6), 0),
              decimal=4,
          )
  
file_path: chainercv/tests/links_tests/model_tests/mobilenet_tests/test_expanded_conv_2d.py
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import testing
  from chainermn import create_communicator
  from chainercv.links.model.mobilenet import ExpandedConv2D
  from chainercv.utils.testing import attr
label_desc: Use image node_id 2 for calling the TestExpandedConv2D obj's underlying member method code with example usage: obj.check_forward(x_data) without return types