code: def start_dynamic_batching(args, tp_engine, waiting_req_list):
      try:
          batch_manager = DynamicBatchManager(
              tp_engine=tp_engine,
              max_total_token_num=args.max_total_token_num,
              batch_max_tokens=args.batch_max_tokens,
              eos_id=args.eos_id,
              model=args.model,
              log_stats=not args.disable_log_stats,
              log_stats_interval=args.log_stats_interval,
              waiting_req_list=waiting_req_list,
          )
  
      except Exception:
          raise Exception
  
      return batch_manager
  
imports: import time
  from typing import List
  from .dynamic_batching.get_tokenizer import get_tokenizer
  from .dynamic_batching.infer_batch import InferBatch
  from .dynamic_batching.io_struct import Batch, Req
  from .dynamic_batching.req_queue import ReqQueue
  from .dynamic_batching.sampling_params import SamplingParams
  from .dynamic_batching.stats import Stats
  from .tensor_parallel import TPInferEngine
label_desc: Use image node_id 1 for calling a global function with example usage: start_dynamic_batching(args, tp_engine, waiting_req_list) and returns: batch_manager