code: def testMultivariateNormalNd(self, event_size, batch_size):
      self.evaluate(
          assert_mvn_target_conservation(event_size, batch_size)
      )
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.mcmc import nuts_autobatching
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import sample
label_desc: Use image node_id 6 for calling the NutsTest obj's underlying member method code with example usage: obj.testMultivariateNormalNd(event_size, batch_size) without return types