code: def test_dlt_aggregated_with_regression(
      make_weekly_data, estimator, regressor_signs, point_method
  ):
      train_df, test_df, coef = make_weekly_data
  
      dlt = DLT(
          response_col="response",
          date_col="week",
          regressor_col=train_df.columns.tolist()[2:],
          regressor_sign=regressor_signs,
          seasonality=52,
          num_warmup=50,
          verbose=False,
          estimator=estimator,
      )
  
      dlt.fit(train_df, point_method=point_method)
      predict_df = dlt.predict(test_df)
  
      regression_out = dlt.get_regression_coefs()
      num_regressors = regression_out.shape[0]
  
      expected_columns = ["week", "prediction"]
      expected_shape = (51, len(expected_columns))
      expected_regression_shape = (6, 7)
  
      assert predict_df.shape == expected_shape
      assert predict_df.columns.tolist() == expected_columns
      assert regression_out.shape == expected_regression_shape
      assert num_regressors == len(train_df.columns.tolist()[2:])
  
      predict_df = dlt.predict(test_df, decompose=True)
      assert any(predict_df["regression"].values)
  
imports: from copy import copy
  import pytest
  import numpy
  from orbit.models import DLT
  from orbit.constants.constants import PredictionKeys
  from orbit.exceptions import ModelException, PredictionException
label_desc: Use image node_id 9 for calling a global function with example usage: test_dlt_aggregated_with_regression(make_weekly_data, estimator, regressor_signs, point_method) without return types