class_docstr: Experimental: Ray distributed tqdm implementation.
  
  This class lets you use tqdm from any Ray remote task or actor, and have the
  progress centrally reported from the driver. This avoids issues with overlapping
  / conflicting progress bars, as the driver centrally manages tqdm positions.
  
  Supports a limited subset of tqdm args.
class_docstr_tok: ["Experimental",":","Ray","distributed","tqdm","implementation",".","This","class","lets","you","use","tqdm","from","any","Ray","remote","task","or","actor",",","and","have","the","progress","centrally","reported","from","the","driver",".","This","avoids","issues","with","overlapping","\/","conflicting","progress","bars",",","as","the","driver","centrally","manages","tqdm","positions",".","Supports","a","limited","subset","of","tqdm","args","."]
code: def _dump_state(self, force_flush=False) -> None:
      now = time.time()
      if (
          not force_flush
          and now - self._last_flush_time < self._flush_interval_s
      ):
          return
      self._last_flush_time = now
      if ray._private.worker.global_worker.mode == ray.WORKER_MODE:
          # Include newline in payload to avoid split prints.
          # TODO(ekl) we should move this to events.json to avoid log corruption.
          print(json.dumps(self._get_state()) + "\n", end="")
      else:
          instance().process_state_update(
              copy.deepcopy(self._get_state())
          )
  
imports: import builtins
  import copy
  import json
  import logging
  import os
  import sys
  import threading
  import time
  import uuid
  from typing import Any, Dict, Iterable, Optional
  import colorama
  import ray
  from ray._private.ray_constants import env_bool
  from ray.util.debug import log_once
label_desc: Use image node_id 8 for calling the tqdm obj's underlying member method code with example usage: obj._dump_state(force_flush) without return types