code: def setUp(self):
      self.mean = numpy.random.uniform(-1, 1, (3,)).astype(
          numpy.float32
      )
      self.ln_var = numpy.random.uniform(-1, 1, (3,)).astype(
          numpy.float32
      )
  
      # Refer to Appendix B in the original paper
      # Auto-Encoding Variational Bayes (https://arxiv.org/abs/1312.6114)
      loss = (
          -(
              1
              + self.ln_var
              - self.mean * self.mean
              - numpy.exp(self.ln_var)
          )
          * 0.5
      )
      if self.reduce == "sum":
          self.expect = numpy.sum(loss)
      elif self.reduce == "mean":
          self.expect = numpy.mean(loss)
      elif self.reduce == "no":
          self.expect = loss
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
label_desc: Use image node_id 1 for calling the TestGaussianKLDivergence obj's underlying member method code with example usage: obj.setUp() without return types