class_docstr: The trainer can train a list of models.
  There are Trainer and DelayTrainer, which can be distinguished by when it will finish real training.
class_docstr_tok: ["The","trainer","can","train","a","list","of","models",".","There","are","Trainer","and","DelayTrainer",",","which","can","be","distinguished","by","when","it","will","finish","real","training","."]
docstr: Some trainer has backend worker to support parallel training
  This method can tell if the worker is enabled.
  
  Returns
  -------
  bool:
      if the worker is enabled
docstr_tok: ["Some","trainer","has","backend","worker","to","support","parallel","training","This","method","can","tell","if","the","worker","is","enabled",".","Returns","--","--","--","-","bool",":","if","the","worker","is","enabled"]
code: def has_worker(self) -> bool:
      """
      Some trainer has backend worker to support parallel training
      This method can tell if the worker is enabled.
  
      Returns
      -------
      bool:
          if the worker is enabled
  
      """
      return False
  
imports: import socket
  from typing import Callable, List, Optional
  from tqdm.auto import tqdm
  from qlib.config import C
  from qlib.data.dataset import Dataset
  from qlib.data.dataset.weight import Reweighter
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import auto_filter_kwargs, fill_placeholder, flatten_dict, init_instance_by_config
  from qlib.utils.paral import call_in_subproc
  from qlib.workflow import R
  from qlib.workflow.recorder import Recorder
  from qlib.workflow.task.manage import TaskManager, run_task
label_desc: Use image node_id 6 for calling the Trainer obj's underlying member method code with example usage: obj.has_worker() and returns: False