code: def check_backward_consistency_regression(self, backend_config):
      # Regression test to two-dimensional unpooling layer.
  
      (x_data,) = self.generate_inputs()
      gy_data = numpy.random.uniform(-1, 1, self.gy_shape).astype(
          self.dtype
      )
  
      ksize = self.ksize
      stride = self.stride
      pad = self.pad
      xp = backend.get_array_module(x_data)
  
      # Backward computation for N-dimensional unpooling layer.
      x_nd = chainer.Variable(xp.array(x_data))
      y_nd = functions.unpooling_nd(
          x_nd, ksize, stride=stride, pad=pad, cover_all=self.cover_all
      )
      y_nd.grad = gy_data
      y_nd.backward()
  
      # Backward computation for two-dimensional unpooling layer.
      x_2d = chainer.Variable(xp.array(x_data))
      y_2d = functions.unpooling_2d(
          x_2d, ksize, stride=stride, pad=pad, cover_all=self.cover_all
      )
      y_2d.grad = gy_data
      y_2d.backward()
  
      # Test that the two result gradients are close enough.
      opt = self.check_backward_options
      testing.assert_allclose(
          x_nd.grad, x_2d.grad, atol=opt["atol"], rtol=opt["rtol"]
      )
  
imports: import itertools
  import unittest
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer import functions
  from chainer import testing
  from chainer.utils import conv
  from chainer.utils import type_check
label_desc: Use image node_id 7 for calling the TestUnpoolingND obj's underlying member method code with example usage: obj.check_backward_consistency_regression(backend_config) without return types