code: def make_uniform_dist(
      self,
      is_gpu=False,
      low=None,
      high=None,
      loc=None,
      scale=None,
      use_loc_scale=False,
  ):
      if use_loc_scale:
          if loc is None:
              loc = numpy.random.uniform(-3, 0, self.shape).astype(
                  numpy.float32
              )
          if scale is None:
              scale = numpy.random.uniform(1, 5, self.shape).astype(
                  numpy.float32
              )
          params = self.encode_params(
              {"loc": loc, "scale": scale}, is_gpu
          )
      else:
          if low is None:
              low = numpy.random.uniform(-3, 0, self.shape).astype(
                  numpy.float32
              )
          if high is None:
              high = numpy.random.uniform(
                  low + 1, low + 6, self.shape
              ).astype(numpy.float32)
          params = self.encode_params(
              {"low": low, "high": high}, is_gpu
          )
      return distributions.Uniform(**params)
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import distributions
  from chainer import testing
  from chainer.testing import attr
label_desc: Use image node_id 18 for calling the TestKLDivergence obj's underlying member method code with example usage: obj.make_uniform_dist(is_gpu, low, high, loc, scale, use_loc_scale) and returns: distributions