code: def setUp(self):
      # Spacial pyramid pooling uses max pooling in its implementation.
      # To avoid instability of numerical gradient, use enough different
      # values.
      shape = (self.n, self.c, self.h, self.w)
      self.x = pooling_nd_helper.shuffled_linspace(shape, self.dtype)
  
      self.one = numpy.ones((self.n, self.c, self.h, self.w)).astype(
          self.dtype
      )
      self.gy = numpy.random.uniform(
          -1, 1, (self.n, self.output_dim, 1, 1)
      ).astype(self.dtype)
      self.ggx = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  from chainer.utils import type_check
  from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
label_desc: Use image node_id 1 for calling the TestSpatialPyramidPooling2D obj's underlying member method code with example usage: obj.setUp() without return types