code: def testCovarianceWithBatch(self):
      d = self._build_mvndiag_mixture(
          probs=[0.2, 0.3, 0.5],
          loc=np.zeros((2, 1, 5, 3, 4)),
          scale_diag=np.ones((2, 1, 5, 3, 4)) * [[1.0], [0.75], [0.5]],
      )
      self.assertAllEqual(
          (2, 1, 5, 4, 4), self.evaluate(d.covariance()).shape
      )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 8 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj.testCovarianceWithBatch() without return types