code: def test_dlt_grid_tuning(make_weekly_data, param_grid, eval_method):
      train_df, test_df, coef = make_weekly_data
      args = {
          "response_col": "response",
          "date_col": "week",
          "seasonality": 52,
          "estimator": "stan-map",
      }
      dlt = DLT(**args)
  
      best_params, tuned_df = grid_search_orbit(
          param_grid,
          model=dlt,
          df=train_df,
          eval_method=eval_method,
          min_train_len=80,
          incremental_len=20,
          forecast_len=20,
      )
  
      assert best_params[0].keys() == param_grid.keys()
      assert set(tuned_df.columns.to_list()) == set(
          list(param_grid.keys()) + ["metrics"]
      )
      assert tuned_df.shape == (9, 3)
  
imports: import pytest
  from orbit.models import DLT, LGT
  from orbit.utils.params_tuning import grid_search_orbit
label_desc: Use image node_id 1 for calling a global function with example usage: test_dlt_grid_tuning(make_weekly_data, param_grid, eval_method) without return types