code: def testPosteriorMode(self):
      gm = mixture_same_family.MixtureSameFamily(
          mixture_distribution=categorical.Categorical(
              probs=self._build_tensor([[0.5, 0.5], [0.01, 0.99]])
          ),
          components_distribution=normal.Normal(
              loc=self._build_tensor([[-1.0, 1.0], [-1.0, 1.0]]),
              scale=self._build_tensor(1.0),
          ),
      )
      mode = gm.posterior_mode(
          self._build_tensor([[1.0], [-1.0], [-6.0]])
      )
      self.assertAllEqual([3, 2], self._shape(mode))
      self.assertAllEqual([[1, 1], [0, 1], [0, 0]], self.evaluate(mode))
  
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 14 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj.testPosteriorMode() without return types