code: def test_deterministic_initialization_from_seed(self):
      initial_samples = []
      seed = test_util.test_seed(sampler_type="stateless")
      init_seed, sample_seed = samplers.split_seed(seed)
      for _ in range(2):
          surrogate_posterior = self._initialize_surrogate(
              "build_affine_surrogate_posterior",
              event_shape={
                  "x": tf.TensorShape([3]),
                  "y": tf.TensorShape([]),
              },
              operators="tril",
              bijector=None,
              dtype=tf.float32,
              seed=init_seed,
              validate_args=True,
          )
          initial_samples.append(
              surrogate_posterior.sample([5], seed=sample_seed)
          )
      self.assertAllEqualNested(initial_samples[0], initial_samples[1])
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import fill_triangular
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import masked_autoregressive
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import laplace
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.experimental.vi import surrogate_posteriors
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  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.vi import optimization
  from tensorflow.python.util import nest
label_desc: Use image node_id 5 for calling the AffineSurrogatePosterior obj's underlying member method code with example usage: obj.test_deterministic_initialization_from_seed() without return types