class_docstr: Reader object that loads experiences from JSON file chunks.
  
  The input files will be read from in random order.
class_docstr_tok: ["Reader","object","that","loads","experiences","from","JSON","file","chunks",".","The","input","files","will","be","read","from","in","random","order","."]
code: def _postprocess_if_needed(
      self, batch: SampleBatchType
  ) -> SampleBatchType:
      if not self.ioctx.config.get("postprocess_inputs"):
          return batch
  
      batch = convert_ma_batch_to_sample_batch(batch)
  
      if isinstance(batch, SampleBatch):
          out = []
          for sub_batch in batch.split_by_episode():
              out.append(
                  self.default_policy.postprocess_trajectory(sub_batch)
              )
          return concat_samples(out)
      else:
          # TODO(ekl) this is trickier since the alignments between agent
          #  trajectories in the episode are not available any more.
          raise NotImplementedError(
              "Postprocessing of multi-agent data not implemented yet."
          )
  
imports: import glob
  import json
  import logging
  import math
  import numpy
  import os
  from pathlib import Path
  import random
  import re
  import tree
  from typing import List, Optional, TYPE_CHECKING, Union
  from urllib.parse import urlparse
  import zipfile
  from ray.rllib.offline.input_reader import InputReader
  from ray.rllib.offline.io_context import IOContext
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, MultiAgentBatch, SampleBatch, concat_samples, convert_ma_batch_to_sample_batch
  from ray.rllib.utils.annotations import override, PublicAPI, DeveloperAPI
  from ray.rllib.utils.compression import unpack_if_needed
  from ray.rllib.utils.spaces.space_utils import clip_action, normalize_action
  from ray.rllib.utils.typing import Any, FileType, SampleBatchType
label_desc: Use image node_id 4 for calling the JsonReader obj's underlying member method code with example usage: obj._postprocess_if_needed(batch) and returns: batch, concat_samples