code: def test_docstring_example_normal(self):
      if not JAX_MODE:
          self.skipTest("Stateless minimization requires optax.")
      import optax  # pylint: disable=g-import-not-at-top
  
      samples = [
          4.57,
          6.37,
          5.93,
          7.98,
          2.03,
          3.59,
          8.55,
          3.45,
          5.06,
          6.44,
      ]
      init_fn, apply_fn = trainable.make_trainable_stateless(
          normal.Normal
      )
      final_params, losses = minimize_stateless(
          lambda *params: -apply_fn(params).log_prob(samples),
          init=init_fn(
              seed=test_util.test_seed(sampler_type="stateless")
          ),
          optimizer=optax.adam(0.1),
          num_steps=200,
      )
      model = apply_fn(final_params)
      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 10 for calling the TestStatelessTrainableDistributionsAndBijectors obj's underlying member method code with example usage: obj.test_docstring_example_normal() without return types