class_docstr: A TFPolicy that auto-defines placeholders dynamically at runtime.
  
  This class is intended to be used and extended by sub-classing.
docstr: Post process trajectory in the format of a SampleBatch.
  
  Args:
      sample_batch: sample_batch: batch of experiences for the policy,
          which will contain at most one episode trajectory.
      other_agent_batches: In a multi-agent env, this contains a
          mapping of agent ids to (policy, agent_batch) tuples
          containing the policy and experiences of the other agents.
      episode: An optional multi-agent episode object to provide
          access to all of the internal episode state, which may
          be useful for model-based or multi-agent algorithms.
  
  Returns:
      The postprocessed sample batch.
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code: def postprocess_trajectory(
      self,
      sample_batch: SampleBatch,
      other_agent_batches: Optional[SampleBatch] = None,
      episode: Optional["Episode"] = None,
  ):
      """Post process trajectory in the format of a SampleBatch.
  
      Args:
          sample_batch: sample_batch: batch of experiences for the policy,
              which will contain at most one episode trajectory.
          other_agent_batches: In a multi-agent env, this contains a
              mapping of agent ids to (policy, agent_batch) tuples
              containing the policy and experiences of the other agents.
          episode: An optional multi-agent episode object to provide
              access to all of the internal episode state, which may
              be useful for model-based or multi-agent algorithms.
  
      Returns:
          The postprocessed sample batch.
      """
      return Policy.postprocess_trajectory(self, sample_batch)
  
imports: from collections import OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, Union
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy import TFMultiGPUTowerStack
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import DeveloperAPI, OverrideToImplementCustomLogic, OverrideToImplementCustomLogic_CallToSuperRecommended, is_overridden, override
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
  from ray.util.debug import log_once
label_desc: Use image node_id 16 for calling the DynamicTFPolicyV2 obj's underlying member method code with example usage: obj.postprocess_trajectory(sample_batch, other_agent_batches, episode) and returns: Policy