class_docstr: Ranking agent class.
code: def _train(self, experience, weights):
      with tf.GradientTape() as tape:
          loss = self._loss(experience, weights, training=True).loss
      if self.summaries_enabled:
          with tf.name_scope("Losses/"):
              tf.compat.v2.summary.scalar(
                  name="loss", data=loss, step=self.train_step_counter
              )
      gradients = tape.gradient(loss, self._variables_to_train())
      grads_and_vars = tuple(zip(gradients, self._variables_to_train()))
      if self._summarize_grads_and_vars:
          eager_utils.add_variables_summaries(
              grads_and_vars, self.train_step_counter
          )
          eager_utils.add_gradients_summaries(
              grads_and_vars, self.train_step_counter
          )
      self._optimizer.apply_gradients(grads_and_vars)
      self.train_step_counter.assign_add(1)
      return tf_agent.LossInfo(loss, extra=())
  
imports: import enum
  from typing import Optional, Text
  import tensorflow
  from tf_agents.agents import tf_agent
  from tf_agents.bandits.policies import ranking_policy
  from tf_agents.specs import bandit_spec_utils
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 4 for calling the RankingAgent obj's underlying member method code with example usage: obj._train(experience, weights) and returns: tf_agent