code: def setUp(self):
      self.expander = (None, Ellipsis) + (None,) * self.ndim
      self.aggr_axes = (0,) + tuple(six.moves.range(2, self.ndim + 2))
  
      self.rmax = self.dtype(3)
      self.dmax = self.dtype(5)
  
      self.link = links.BatchRenormalization(
          3,
          rmax=self.rmax,
          dmax=self.dmax,
          dtype=self.dtype,
          eps=self.eps,
      )
      gamma = self.link.gamma.array
      gamma[...] = numpy.random.uniform(0.5, 1, gamma.shape)
      beta = self.link.beta.array
      beta[...] = numpy.random.uniform(-1, 1, beta.shape)
      self.link.cleargrads()
  
      self.gamma = gamma.copy()[self.expander]  # fixed on CPU
      self.beta = beta.copy()[self.expander]  # fixed on CPU
  
      shape = (5, 3) + (2,) * self.ndim
      self.x = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
      self.gy = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
  
      if self.test:
          self.mean = numpy.random.uniform(-1, 1, (3,)).astype(
              self.dtype
          )
          self.var = numpy.random.uniform(0.5, 1, (3,)).astype(
              self.dtype
          )
          self.running_mean = self.mean
          self.running_var = self.var
      else:
          self.mean = self.x.mean(axis=self.aggr_axes)
          self.var = self.x.var(axis=self.aggr_axes)
          # Need to add some noise to running_mean and running_var,
          # otherwise we will always get r=1, d=0
          # Note that numpy.exp(3) > rmax ** 2 and 7 > dmax
          self.running_var = self.var * numpy.exp(
              numpy.random.uniform(-3, 3, self.var.shape)
          ).astype(self.dtype)
          self.running_mean = self.mean + (
              (numpy.sqrt(self.running_var) + 0.1)
              * numpy.random.uniform(-7, 7, self.mean.shape)
          ).astype(self.dtype)
      self.link.avg_mean[...] = self.running_mean
      self.link.avg_var[...] = self.running_var
      self.check_forward_optionss = {"atol": 1e-4, "rtol": 1e-3}
      self.check_backward_optionss = {"atol": 1e-4, "rtol": 1e-3}
      if self.dtype == numpy.float16:
          self.check_forward_optionss = {"atol": 1e-2, "rtol": 5e-3}
          self.check_backward_optionss = {"atol": 5e-1, "rtol": 1e-1}
  
imports: import unittest
  import numpy
  import six
  import chainer
  from chainer.backends import cuda
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
label_desc: Use image node_id 1 for calling the BatchRenormalizationTest obj's underlying member method code with example usage: obj.setUp() without return types