old_contents: # coding=utf-8
  # Copyright 2022 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.
  
  """Adapter for using unbatched observers in batched contexts."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from typing import Callable
  
  from tf_agents.trajectories import trajectory as trajectory_lib
  from tf_agents.utils import nest_utils
  
  Observer = Callable[[trajectory_lib.Trajectory], None]
  
  
  class BatchedObserverUnbatching(object):
      """An observer that takes batched trajectories, unbatches them, and delegates them to multiple observers.
  
      The unbatched trajectories are delegated to observers that don't support
      batch dimensions (e.g. ReverbAddEpisodeObserver).
  
      Note that the batch size is assumed to be fixed and it is not validated.
      """
  
      def __init__(self,
                   create_delegated_observer_fn: Callable[[], Observer],
                   batch_size: int):
          self._delegated_observers = [
              create_delegated_observer_fn() for _ in range(batch_size)
          ]
  
      def __call__(self, batched_trajectory: trajectory_lib.Trajectory):
          unbatched_trajectories = nest_utils.unstack_nested_arrays(
              batched_trajectory)
          for obs, traj in zip(self._delegated_observers, unbatched_trajectories):
              # TODO: parallelize running delegated observers.
              obs(traj)
  
new_contents: # coding=utf-8
  # Copyright 2022 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.
  
  """Adapter for using unbatched observers in batched contexts."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from typing import Callable
  
  from tf_agents.trajectories import trajectory as trajectory_lib
  from tf_agents.utils import nest_utils
  
  
  class BatchedObserverUnbatching(object):
      """An observer that takes batched trajectories, unbatches them, and delegates them to multiple observers.
  
      The unbatched trajectories are delegated to observers that don't support
      batch dimensions (e.g. ReverbAddEpisodeObserver).
  
      Note that the batch size is assumed to be fixed and it is not validated.
      """
  
      def __init__(self,
                   create_delegated_observer_fn: Callable[[], Callable[[trajectory_lib.Trajectory], None]],
                   batch_size: int):
          self._delegated_observers = [
              create_delegated_observer_fn() for _ in range(batch_size)
          ]
  
      def __call__(self, batched_trajectory: trajectory_lib.Trajectory):
          unbatched_trajectories = nest_utils.unstack_nested_arrays(
              batched_trajectory)
          for obs, traj in zip(self._delegated_observers, unbatched_trajectories):
              # TODO: parallelize running delegated observers.
              obs(traj)
  
subject: Address Observer comment.