code: def _make_stateful_optimizer_step_fn(
      loss_fn, optimizer, trainable_variables
  ):
      """Constructs a single step of a stateful (`tf_keras.optimizers`) optimizer."""
  
      @tf.function(autograph=False)
      def optimizer_step(parameters, optimizer_state=None, seed=None):
          """Run a single optimization step."""
          del parameters  # Unused.
          del optimizer_state  # Unused.
          with tf.GradientTape(
              watch_accessed_variables=trainable_variables is None
          ) as tape:
              for v in trainable_variables or []:
                  tape.watch(v)
              try:
                  loss = loss_fn(seed=seed)
              except TypeError:
                  loss = loss_fn()
          watched_variables = tape.watched_variables()
          grads = tape.gradient(loss, watched_variables)
          train_op = optimizer.apply_gradients(
              zip(grads, watched_variables)
          )
          with tf.control_dependencies([train_op]):
              return (
                  tf.identity(loss),
                  [tf.identity(g) for g in grads],
                  [tf.identity(v) for v in watched_variables],
                  (),
              )
  
      return optimizer_step
  
file_path: probability/tensorflow_probability/python/math/minimize.py
imports: import collections
  import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import loop_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.math.gradient import value_and_gradient
label_desc: Use image node_id 7 for calling a global function with example usage: _make_stateful_optimizer_step_fn(loss_fn, optimizer, trainable_variables) and returns: optimizer_step, tf,  unknown,  unknown,  