class_docstr: Thread to continuously iterate over the fetchers files and add them to a threading.Queue
  
  Note: We are making the assumption that there will only be one of these running per process.
  If we add more, then the base fetcher will need to support locking around the _file_records
  dictionary. Defined in <ROOT>/harness/determined/tensorboard/fetchers/base.py
class_docstr_tok: ["Thread","to","continuously","iterate","over","the","fetchers","files","and","add","them","to","a","threading.Queue","Note",":","We","are","making","the","assumption","that","there","will","only","be","one","of","these","running","per","process",".","If","we","add","more",",","then","the","base","fetcher","will","need","to","support","locking","around","the","_file_records","dictionary",".","Defined","in","<","ROOT",">","\/harness\/determined\/tensorboard\/fetchers\/base.py"]
code: def __init__(
      self,
      fetcher: fetchers.Fetcher,
      work_queue: queue.Queue,
      *args: Any,
      **kwargs: Any,
  ) -> None:
      self._fetcher = fetcher
      self._work_queue = work_queue
      super().__init__(*args, **kwargs)
  
imports: import json
  import logging
  import os
  import queue
  import subprocess
  import sys
  import tempfile
  import threading
  import time
  from typing import Any, Callable, Dict, List
  import boto3
  import requests
  from packaging import version
  import determined
  import determined.common
  from determined.tensorboard import fetchers
label_desc: Use image node_id 1 to create a new TBFetchIterationThread object from inherited base classes: threading with example: obj = TBFetchIterationThread(fetcher, work_queue)