code: def testVarianceConsistentCovariance(self):
      gm = self._build_mvndiag_mixture(
          probs=[0.3, 0.7],
          loc=[[-1.0, 1], [1, -1]],
          scale_diag=[[1.0, 1.0], [0.5, 0.5]],
      )
      cov_, var_ = self.evaluate([gm.covariance(), gm.variance()])
      self.assertAllClose(cov_.diagonal(), var_, atol=0.0)
  
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 10 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj.testVarianceConsistentCovariance() without return types