old_contents: import unittest
  
  from chainer import distributions
  from chainer import testing
  import numpy
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3, 2), (1,)],
      'is_variable': [True, False],
      'sample_shape': [(3, 2), ()],
  }))
  @testing.fix_random()
  @testing.with_requires('scipy')
  class TestMultivariateNormal(testing.distribution_unittest):
  
      def setUp(self):
          testing.distribution_unittest.setUp(self)
          from scipy import stats
          self.dist = distributions.MultivariateNormal
          self.scipy_dist = stats.multivariate_normal
          self.scipy_onebyone = True
          self.event_shape = (3,)
  
          self.test_targets = set([
              "batch_shape", "entropy", "event_shape", "log_prob",
              "support"])
  
      def params_init(self):
          loc = numpy.random.uniform(
              -1, 1, self.shape + (3,)).astype(numpy.float32)
          cov = numpy.random.normal(size=self.shape + (3, 3))
          cov = numpy.matmul(
              cov, numpy.rollaxis(cov, -1, -2)).astype(numpy.float32)
          scale_tril = numpy.linalg.cholesky(cov).astype(numpy.float32)
          self.params = {"loc": loc, "scale_tril": scale_tril}
          self.scipy_params = {"mean": loc, "cov": cov}
  
      def sample_for_test(self):
          smp = numpy.random.normal(
              size=self.sample_shape + self.shape + (3,)).astype(numpy.float32)
          return smp
  
  
  testing.run_module(__name__, __file__)
  
new_contents: from chainer import distributions
  from chainer import testing
  import numpy
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3, 2), (1,)],
      'is_variable': [True, False],
      'sample_shape': [(3, 2), ()],
  }))
  @testing.fix_random()
  @testing.with_requires('scipy')
  class TestMultivariateNormal(testing.distribution_unittest):
  
      def setUp(self):
          testing.distribution_unittest.setUp(self)
          from scipy import stats
          self.dist = distributions.MultivariateNormal
          self.scipy_dist = stats.multivariate_normal
          self.scipy_onebyone = True
          self.event_shape = (3,)
  
          self.test_targets = set([
              "batch_shape", "entropy", "event_shape", "log_prob",
              "support"])
  
      def params_init(self):
          loc = numpy.random.uniform(
              -1, 1, self.shape + (3,)).astype(numpy.float32)
          cov = numpy.random.normal(size=self.shape + (3, 3))
          cov = numpy.matmul(
              cov, numpy.rollaxis(cov, -1, -2)).astype(numpy.float32)
          scale_tril = numpy.linalg.cholesky(cov).astype(numpy.float32)
          self.params = {"loc": loc, "scale_tril": scale_tril}
          self.scipy_params = {"mean": loc, "cov": cov}
  
      def sample_for_test(self):
          smp = numpy.random.normal(
              size=self.sample_shape + self.shape + (3,)).astype(numpy.float32)
          return smp
  
  
  testing.run_module(__name__, __file__)
  
subject: fix unused