code: def setUp(self):
      min_abs = 0.1
      if self.dtype == numpy.float16:
          tuple_axis = self.axis
          if not isinstance(tuple_axis, tuple):
              tuple_axis = (tuple_axis,)
          aggr_size = numpy.prod(
              [self.shape[i] for i in tuple_axis], dtype=int
          )
          min_abs = max(min_abs, 0.5 / aggr_size)
      self.x = chainer.utils.force_array(
          numpy.random.uniform(min_abs, 1, self.shape)
          * (1 - 2 * numpy.random.randint(2, size=self.shape)),
          self.dtype,
      )
      if self.nonzeros is not None:
          # Make self.x have limited number of large values
  
          # get mask of indices to modify at
          zeros = self.x.size - self.nonzeros
          while True:
              rand = numpy.random.uniform(0, 1, self.shape)
              mask = rand <= numpy.sort(rand.ravel())[zeros - 1]
              if self.x[mask].shape == (zeros,):
                  break
  
          # set zeros or small values to a part of the input
          if self.truezero:
              self.x[mask] = 0
          else:
              zero_scale = 10.0 ** numpy.random.randint(-40, -3)
              self.x[mask] = numpy.random.uniform(
                  -zero_scale, zero_scale, zeros
              )
      self.gy = numpy.random.uniform(-1, 1, self.shape).astype(
          self.dtype
      )
      self.ggx = numpy.random.uniform(-1, 1, self.shape).astype(
          self.dtype
      )
  
      if self.dtype == numpy.float16:
          self.check_forward_options = {"atol": 1e-3, "rtol": 1e-3}
      else:
          self.check_forward_options = {}
  
      if self.nonzeros is None:
          if self.dtype == numpy.float16:
              self.check_backward_options = {
                  "dtype": numpy.float64,
                  "atol": 5e-3,
                  "rtol": 5e-3,
              }
              self.check_double_backward_options = {
                  "dtype": numpy.float64,
                  "atol": 1e-2,
                  "rtol": 1e-2,
              }
          else:
              self.check_backward_options = {
                  "dtype": numpy.float64,
                  "atol": 1e-4,
                  "rtol": 1e-4,
              }
              self.check_double_backward_options = {
                  "dtype": numpy.float64,
                  "atol": 1e-4,
                  "rtol": 1e-4,
              }
      else:
          self.check_backward_options = {
              "dtype": numpy.float64,
              "atol": 1e-2,
              "rtol": 1e-2,
              "eps": 1e-4,
          }
  
imports: import functools
  import itertools
  import unittest
  import numpy
  import six
  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
label_desc: Use image node_id 1 for calling the TestL2Normalization obj's underlying member method code with example usage: obj.setUp() without return types