code: def check_backward(self, x_data, W_data, b_data, y_grad):
      xp = backend.get_array_module(x_data)
      if not self.c_contiguous:
          x_data = xp.asfortranarray(x_data)
          W_data = xp.asfortranarray(W_data)
          y_grad = xp.asfortranarray(y_grad)
          self.assertFalse(x_data.flags.c_contiguous)
          self.assertFalse(W_data.flags.c_contiguous)
          self.assertFalse(y_grad.flags.c_contiguous)
          if b_data is not None:
              b = xp.empty((len(b_data) * 2,), dtype=self.b.dtype)
              b[::2] = b_data
              b_data = b[::2]
              self.assertFalse(b_data.flags.c_contiguous)
  
      args = (x_data, W_data)
      if b_data is not None:
          args = args + (b_data,)
  
      def f(*args):
          return F.dilated_convolution_2d(
              *args,
              stride=self.stride,
              pad=self.pad,
              dilate=self.dilate,
              cover_all=self.cover_all,
          )
  
      with chainer.using_config("use_cudnn", self.use_cudnn):
          gradient_check.check_backward(
              f,
              args,
              y_grad,
              dtype=numpy.float64,
              **self.check_backward_options,
          )
  
imports: import unittest
  import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  import chainer.functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
label_desc: Use image node_id 5 for calling the TestDilatedConvolution2DFunction obj's underlying member method code with example usage: obj.check_backward(x_data, W_data, b_data, y_grad) and returns: F