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.
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docstr: Given a list of models, finished something at the end of training if you need.
  The models may be Recorder, txt file, database, and so on.
  
  For Trainer, it does some finishing touches in this method.
  For DelayTrainer, it finishes real training in this method.
  
  Args:
      models: a list of models
  
  Returns:
      list: a list of models
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code: def end_train(self, models: list, *args, **kwargs) -> list:
      """
      Given a list of models, finished something at the end of training if you need.
      The models may be Recorder, txt file, database, and so on.
  
      For Trainer, it does some finishing touches in this method.
      For DelayTrainer, it finishes real training in this method.
  
      Args:
          models: a list of models
  
      Returns:
          list: a list of models
      """
      # do nothing if you finished all work in `train` method
      return models
  
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 3 for calling the Trainer obj's underlying member method code with example usage: obj.end_train(models) and returns: models