code: def test_initialization_is_deterministic_with_seed(self):
      seed = test_util.test_seed(sampler_type="stateless")
      distribution1 = trainable.make_trainable(
          normal.Normal, seed=seed, validate_args=True
      )
      self.evaluate(
          [v.initializer for v in distribution1.trainable_variables]
      )
  
      distribution2 = trainable.make_trainable(
          normal.Normal, seed=seed, validate_args=True
      )
      self.evaluate(
          [v.initializer for v in distribution2.trainable_variables]
      )
  
      self.assertAllEqual(distribution1.mean(), distribution2.mean())
      self.assertAllEqual(
          distribution1.stddev(), distribution2.stddev()
      )
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import geometric
  from tensorflow_probability.python.distributions import kumaraswamy
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.experimental.util import trainable
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.minimize import minimize
  from tensorflow_probability.python.math.minimize import minimize_stateless
label_desc: Use image node_id 5 for calling the TestStatefulMakeTrainable obj's underlying member method code with example usage: obj.test_initialization_is_deterministic_with_seed() without return types