text: from src.scripts.io_sagemaker import hpo_job_create, hpo_job_list
  from src.scripts.io_ssm import ssm_get_param
  
  def model_train():
    """Get model training config and start training job."""
    pass
  
  def model_train_hpo(sm_client, ssm_client, model_type, model_label, model_metric, tuning_job_name):
    """Return the hyperparameter training job's arn, if successfully started."""
    param_prefix = "hpo_"
    if model_type == "xgb":
      if model_label == "1x":
        from src.models.xgboost.xgb_1x import xgboost_1x
        return xgboost_1x(
          sm_client, ssm_client, model_label, model_metric, tuning_job_name, param_prefix
        )
  
      elif model_label == "x2":
        from src.models.xgboost.xgb_x2 import xgboost_x2
        return xgboost_x2(
          sm_client, ssm_client, model_label, model_metric, tuning_job_name, param_prefix
        )
  
      elif model_label == "12":
        from src.models.xgboost.xgb_12 import xgboost_12
        return xgboost_12(
          sm_client, ssm_client, model_label, model_metric, tuning_job_name, param_prefix
        )
  
      elif model_label == "xc":
        from src.models.xgboost.xgb_xc import xgboost_xc
        return xgboost_xc(
          sm_client, ssm_client, model_label, model_metric, tuning_job_name, param_prefix
        )
  
    else:
      raise ValueError(f"Model type '{model_type}' is not valid.")
  