code: def start_tensorboard(
      storage_config: Dict[str, Any],
      tb_version: str,
      storage_paths: List[str],
      add_tb_args: List[str],
  ) -> int:
      """Start Tensorboard and look for new files."""
      with tempfile.TemporaryDirectory() as local_dir:
          # Get fetcher and perform initial fetch
          logger.debug(
              f"Building fetcher...\n"
              f"\tstorage_config: {storage_config}\n"
              f"\tstorage_paths: {storage_paths}\n"
              f"\tlocal_dir: {local_dir}"
          )
          fetcher = fetchers.build(
              storage_config, storage_paths, local_dir
          )
  
          # Build Tensorboard args and launch process.
          tb_args = get_tensorboard_args(
              tb_version, local_dir, add_tb_args
          )
          logger.debug(f"tensorboard args: {tb_args}")
          tensorboard_process = subprocess.Popen(tb_args)
          tb_fetch_manager = TBFetchManager()
          work_queue: queue.Queue = queue.Queue(
              maxsize=WORK_QUEUE_MAX_SIZE
          )
  
          iteration_thread = TBFetchIterationThread(
              fetcher=fetcher, work_queue=work_queue, daemon=True
          )
          fetch_threads = [
              TBFetchThread(
                  fetcher=fetcher,
                  work_queue=work_queue,
                  new_file_callback=tb_fetch_manager.on_file_fetched,
                  daemon=True,
              )
              for _ in range(NUM_FETCH_THREADS)
          ]
  
          with det.util.forward_signals(tensorboard_process):
              try:
                  tb_unresponsive_stop_time = (
                      time.time() + TB_RESPONSE_WAIT_TIME
                  )
  
                  # Wait for the Tensorboard process to start responding before proceeding.
                  responsive = False
                  while not responsive:
                      raise_if_dead(tensorboard_process)
  
                      if time.time() > tb_unresponsive_stop_time:
                          raise RuntimeError(
                              "Tensorboard wasn't responsive before the timeout."
                          )
  
                      time.sleep(TICK_INTERVAL)
                      responsive = check_tensorboard_responsive()
  
                  # Continuously loop checking for new files
                  stop_time = time.time() + MAX_WAIT_TIME
                  iteration_thread.start()
                  for fetch_thread in fetch_threads:
                      fetch_thread.start()
                  while True:
                      raise_if_dead(tensorboard_process)
  
                      # Check if we have reached a timeout without downloading any files
                      if tb_fetch_manager.get_num_fetched_files() == 0:
                          if time.time() > stop_time:
                              raise RuntimeError(
                                  "No new files were fetched before the timeout."
                              )
                          else:
                              # TODO: This should trigger an actual task state change (DET-10001).
                              # For now, just print a message to the logs.
                              print(
                                  "TensorBoard is waiting for metrics.",
                                  flush=True,
                              )
                      time.sleep(TICK_INTERVAL)
  
              finally:
                  if tensorboard_process.poll() is None:
                      logger.info("Killing tensorboard process")
                      tensorboard_process.kill()
  
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 6 for calling a global function with example usage: start_tensorboard(storage_config, tb_version, storage_paths, add_tb_args) without return types