code: def testCovarianceMultidimensional(self):
      # Shape [3, 5, 4]
      p = np.random.dirichlet([0.25, 0.25, 0.25, 0.25], [3, 5]).astype(
          np.float32
      )
      # Shape [6, 3, 3]
      p2 = np.random.dirichlet([0.3, 0.3, 0.4], [6, 3]).astype(
          np.float32
      )
  
      ns = np.random.randint(low=1, high=11, size=[3, 5]).astype(
          np.float32
      )
      ns2 = np.random.randint(low=1, high=11, size=[6, 1]).astype(
          np.float32
      )
  
      dist = multinomial.Multinomial(ns, p, validate_args=True)
      dist2 = multinomial.Multinomial(ns2, p2, validate_args=True)
  
      covariance = dist.covariance()
      covariance2 = dist2.covariance()
      self.assertEqual((3, 5, 4, 4), covariance.shape)
      self.assertEqual((6, 3, 3, 3), covariance2.shape)
  
imports: import unittest
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 22 for calling the MultinomialTest obj's underlying member method code with example usage: obj.testCovarianceMultidimensional() without return types