code: def testCovarianceMultidimensional(self):
      alpha = np.random.rand(3, 1, 4).astype(np.float32)
      alpha2 = np.random.rand(6, 3, 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 = dm.DirichletMultinomial(ns, alpha, validate_args=True)
      dist2 = dm.DirichletMultinomial(ns2, alpha2, 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 numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import dirichlet_multinomial
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 20 for calling the DirichletMultinomialTest obj's underlying member method code with example usage: obj.testCovarianceMultidimensional() without return types