code: def setUp(self):
      # x, W, and b are set so that the result of forward
      # propagation gets stable, meaning that their small perturbations
      # do not change :math:`argmax_{j} W_{ij\cdot} x + b_{ij}`.
  
      x_shape = (self.batchsize,) + self.in_shape
      self.x = (
          numpy.random.uniform(-0.05, 0.05, x_shape).astype(
              numpy.float32
          )
          + 1
      )
      self.gy = numpy.random.uniform(
          -0.05, 0.05, (self.batchsize, self.out_size)
      ).astype(numpy.float32)
  
      in_size = numpy.prod(self.in_shape)
      initialW = numpy.random.uniform(
          -0.05, 0.05, (self.out_size, self.pool_size, in_size)
      ).astype(numpy.float32)
      for o in six.moves.range(self.out_size):
          w = numpy.arange(in_size, dtype=numpy.float32) + 1
          for c in six.moves.range(self.pool_size):
              initialW[o, c, :] += w * c
  
      if self.initial_bias == "random":
          initial_bias = numpy.random.uniform(
              -0.05, 0.05, (self.out_size, self.pool_size)
          )
      elif self.initial_bias == "scalar":
          initial_bias = numpy.full(
              (self.out_size, self.pool_size), 5, dtype=numpy.float32
          )
      elif self.initial_bias is None:
          initial_bias = None
  
      self.link = links.Maxout(
          in_size, self.out_size, self.pool_size, initialW, initial_bias
      )
  
      self.y = _maxout(self.x, initialW, initial_bias)
      self.link.cleargrads()
  
imports: import unittest
  import numpy
  import six
  import chainer
  from chainer.backends import cuda
  from chainer import gradient_check
  from chainer.initializers import constant
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling the TestMaxout obj's underlying member method code with example usage: obj.setUp() without return types