code: def testDeterministicSampling(self):
      seed = test_util.test_seed()
      dist = mixture_same_family.MixtureSameFamily(
          mixture_distribution=categorical.Categorical(
              logits=[0.0, 0.0]
          ),
          components_distribution=normal.Normal(
              loc=[0.0, 200.0], scale=[1.0, 1.0]
          ),
          validate_args=True,
      )
      tf.random.set_seed(seed)
      sample_1 = self.evaluate(dist.sample([100], seed=seed))
      tf.random.set_seed(seed)
      sample_2 = self.evaluate(dist.sample([100], seed=seed))
      self.assertAllClose(sample_1, sample_2)
  
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 22 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj.testDeterministicSampling() without return types