code: def test_docstring_example_normal(self):
      samples = [
          4.57,
          6.37,
          5.93,
          7.98,
          2.03,
          3.59,
          8.55,
          3.45,
          5.06,
          6.44,
      ]
      model = trainable.make_trainable(
          normal.Normal,
          seed=test_util.test_seed(sampler_type="stateless"),
      )
      losses = minimize(
          lambda: -model.log_prob(samples),
          optimizer=tf_keras.optimizers.Adam(0.1),
          num_steps=200,
      )
      self.evaluate(tf1.global_variables_initializer())
      self.evaluate(losses)
      self.assertAllClose(
          tf.reduce_mean(samples), model.mean(), atol=2.0
      )
      self.assertAllClose(
          tf.math.reduce_std(samples), model.stddev(), atol=2.0
      )
  
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 9 for calling the TestStatefulMakeTrainable obj's underlying member method code with example usage: obj.test_docstring_example_normal() without return types