code: def test_dlt_full_with_regression(make_weekly_data, regressor_signs):
      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,
          prediction_percentiles=[5, 95],
          seasonality=52,
          num_warmup=50,
          verbose=False,
          estimator="stan-mcmc",
      )
  
      dlt.fit(train_df)
      init_values = dlt._model.get_init_values()
      assert init_values["init_sea"].shape == (51,)
  
      if regressor_signs.count("+") > 0:
          assert init_values["pr_beta"].shape == (
              regressor_signs.count("+"),
          )
      if regressor_signs.count("-") > 0:
          assert init_values["nr_beta"].shape == (
              regressor_signs.count("-"),
          )
      if regressor_signs.count("=") > 0:
          assert init_values["rr_beta"].shape == (
              regressor_signs.count("="),
          )
  
      predict_df = dlt.predict(test_df)
  
      regression_out = dlt.get_regression_coefs()
      num_regressors = regression_out.shape[0]
  
      expected_columns = [
          "week",
          "prediction_5",
          "prediction",
          "prediction_95",
      ]
      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:])
  
      assert np.sum(
          regression_out["coefficient"].values >= 0
      ) <= regressor_signs.count("+") + regressor_signs.count("=")
      assert np.sum(
          regression_out["coefficient"].values <= 0
      ) <= regressor_signs.count("-") + regressor_signs.count("=")
  
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 8 for calling a global function with example usage: test_dlt_full_with_regression(make_weekly_data, regressor_signs) without return types