class_docstr: Marwil's custom trajectory post-processing mixin.
code: def __init__(self):
      pass
  
imports: import logging
  from typing import Any, Dict, List, Optional, Type, Union
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_advantages, Postprocessing
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import ValueNetworkMixin, compute_gradients
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, get_variable
  from ray.rllib.utils.tf_utils import explained_variance
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType
label_desc: Use image node_id 1 to create a new PostprocessAdvantages object  with example: obj = PostprocessAdvantages()