code: def testVariance(self):
      alpha = [1.0, 2, 3]
      denominator = np.sum(alpha) ** 2 * (np.sum(alpha) + 1)
      dirichlet = dirichlet_lib.Dirichlet(concentration=alpha)
      self.assertEqual(dirichlet.covariance().get_shape(), (3, 3))
      if not stats:
          return
      expected_covariance = np.diag(stats.dirichlet.var(alpha))
      expected_covariance += [
          [0.0, -2, -3],
          [-2, 0, -6],
          [-3, -6, 0],
      ] / denominator
      self.assertAllClose(
          self.evaluate(dirichlet.covariance()), expected_covariance
      )
  
imports: import importlib
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.distributions import dirichlet
  from tensorflow.python.ops.distributions import kullback_leibler
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 15 for calling the DirichletTest obj's underlying member method code with example usage: obj.testVariance() without return types