code: def skip_hpo(model, train_data, val_data, time_limit=None):
      """Skip hyperparameter optimization and train model with given parameters.
      Replaces `core.models.abstract.model_trial.skip_hpo` for timeseries
      models.
      """
      fit_and_save_model(
          model=model,
          train_data=train_data,
          val_data=val_data,
          fit_kwargs={},
          eval_metric=model.eval_metric,
          time_start=time.time(),
          time_limit=time_limit,
      )
      hpo_results = {"total_time": model.fit_time}
      hpo_model_performances = {model.name: model.val_score}
      hpo_results["hpo_model_performances"] = hpo_model_performances
      hpo_models = {model.name: model.path}
      return hpo_models, hpo_results
  
file_path: autogluon/timeseries/src/autogluon/timeseries/models/abstract/model_trial.py
imports: import logging
  import os
  import time
  import traceback
  from autogluon.core.models.abstract.model_trial import init_model
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from autogluon.core.utils.loaders import load_pkl
label_desc: Use image node_id 3 for calling a global function with example usage: skip_hpo(model, train_data, val_data, time_limit) and returns: hpo_models,  hpo_results