code: def check_forward(self, x_data):
      x = chainer.Variable(x_data)
      y = self.link(x, train=True, use_batchwise_mask=False)
  
      # check mask equality here.
      testing.assert_allclose(y.data[0], y.data[1])
      testing.assert_allclose(y.data[0], y.data[2])
      testing.assert_allclose(y.data[0], y.data[3])
  
      mask = y.creator.mask
      mask = cuda.to_cpu(mask)
  
      y_expect = self.x.dot(self.W.T * mask.T) * (
          1.0 / (1 - self.ratio)
      )
      y_expect += self.b
      testing.assert_allclose(y_expect, y.data)
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import gradient_check
  from chainer import links
  from chainer.serializers import npz
  from chainer import testing
  from chainer.testing import attr
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the TestSimplifiedDropconnectNotBatchwiseMask obj's underlying member method code with example usage: obj.check_forward(x_data) without return types