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 _try_parse(self, line: str) -> Optional[SampleBatchType]:
      line = line.strip()
      if not line:
          return None
      try:
          batch = self._from_json(line)
      except Exception:
          logger.exception(
              "Ignoring corrupt json record in {}: {}".format(
                  self.cur_file, line
              )
          )
          return None
  
      batch = postprocess_actions(batch, self.ioctx)
  
      return batch
  
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 6 for calling the JsonReader obj's underlying member method code with example usage: obj._try_parse(line) and returns: batch, None, None