text: # coding=utf-8
  # Copyright 2020 The TF-Agents Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Driver utilities for use with bandit policies and environments."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf  # pylint: disable=g-explicit-tensorflow-version-import
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  
  nest = tf.compat.v2.nest
  
  
  def trajectory_for_bandit(initial_step: types.TimeStep,
                            action_step: types.PolicyStep,
                            final_step: types.TimeStep) -> types.NestedTensor:
    """Builds a trajectory from a single-step bandit episode.
  
    Since all episodes consist of a single step, the returned `Trajectory` has no
    time dimension. All input and output `Tensor`s/arrays are expected to have
    shape `[batch_size, ...]`.
  
    Args:
      initial_step: A `TimeStep` returned from `environment.step(...)`.
      action_step: A `PolicyStep` returned by `policy.action(...)`.
      final_step: A `TimeStep` returned from `environment.step(...)`.
    Returns:
      A `Trajectory` containing zeros for discount value and `StepType.LAST` for
      both `step_type` and `next_step_type`.
    """
    return trajectory.Trajectory(observation=initial_step.observation,
                                 action=action_step.action,
                                 policy_info=action_step.info,
                                 reward=final_step.reward,
                                 discount=final_step.discount,
                                 step_type=initial_step.step_type,
                                 next_step_type=final_step.step_type)
  