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","."]
code: def __call__(self, *args, **kwargs) -> list:
      return self.end_train(self.train(*args, **kwargs))
  
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 5 for calling the Trainer obj's underlying member method code with example usage: obj.__call__() and returns: self