code: def check_backward_consistency_regression(
      self, x_data, gy_data, backend_config
  ):
      # Regression test to two-dimensional average pooling layer.
      ksize = self.ksize
      stride = self.stride
      pad = self.pad
      pad_value = self.pad_value
  
      # Backward computation for N-dimensional average pooling layer.
      x_nd = chainer.Variable(x_data)
      with backend_config:
          y_nd = functions.average_pooling_nd(
              x_nd, ksize, stride=stride, pad=pad, pad_value=pad_value
          )
      y_nd.grad = gy_data
      y_nd.backward()
  
      # Backward computation for two-dimensional average pooling layer.
      x_2d = chainer.Variable(x_data)
      with backend_config:
          y_2d = functions.average_pooling_2d(
              x_2d, ksize, stride=stride, pad=pad
          )
      y_2d.grad = gy_data
      y_2d.backward()
  
      # Test that the two result gradients are close enough.
      testing.assert_allclose(x_nd.grad, x_2d.grad, **self.tolerance)
  
imports: import functools
  import operator
  import unittest
  import numpy
  import pytest
  import six
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.utils import conv
  from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
label_desc: Use image node_id 4 for calling the TestConsistencyAveragePoolingND obj's underlying member method code with example usage: obj.check_backward_consistency_regression(x_data, gy_data, backend_config) without return types