code: def make_multivariatenormal_dist(self, is_gpu=False):
      loc = numpy.random.uniform(-1, 1, self.shape + (3,)).astype(
          numpy.float32
      )
      cov = numpy.random.normal(size=(numpy.prod(self.shape),) + (3, 3))
      cov = [cov_.dot(cov_.T) for cov_ in cov]
      cov = numpy.vstack(cov).reshape(self.shape + (3, 3))
      scale_tril = numpy.linalg.cholesky(cov).astype(numpy.float32)
      params = self.encode_params(
          {"loc": loc, "scale_tril": scale_tril}, is_gpu
      )
      return distributions.MultivariateNormal(**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 14 for calling the TestKLDivergence obj's underlying member method code with example usage: obj.make_multivariatenormal_dist(is_gpu) and returns: distributions