code: def setUp(self):
      self.x = numpy.random.uniform(-1, 1, (3,)).astype(numpy.float32)
      self.y = numpy.random.uniform(-1, 1, (3,)).astype(numpy.float32)
  
      # Refer to Appendix C.1 in the original paper
      # Auto-Encoding Variational Bayes (https://arxiv.org/abs/1312.6114)
      p = 1 / (1 + numpy.exp(-self.y))
      self.expect = -(
          self.x * numpy.log(p) + (1 - self.x) * numpy.log(1 - p)
      )
      if self.reduce == "sum":
          self.expect = numpy.sum(self.expect)
      elif self.reduce == "mean":
          self.expect = numpy.mean(self.expect)
  
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 TestBernoulliNLL obj's underlying member method code with example usage: obj.setUp() without return types