code: def test_dlt_map_single_regressor(iclaims_training_data):
      df = iclaims_training_data
      df["claims"] = np.log(df["claims"])
      regressor_col = ["trend.unemploy"]
  
      dlt = DLT(
          response_col="claims",
          date_col="week",
          regressor_col=regressor_col,
          seasonality=52,
          seed=8888,
          estimator="stan-map",
      )
      dlt.fit(df)
      predicted_df = dlt.predict(df)
  
      expected_num_parameters = (
          len(dlt._model.get_model_param_names()) + 1
      )
      expected_columns = ["week", "prediction"]
  
      assert predicted_df.shape[0] == df.shape[0]
      assert predicted_df.columns.tolist() == expected_columns
      assert len(dlt._posterior_samples) == expected_num_parameters
  
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 18 for calling a global function with example usage: test_dlt_map_single_regressor(iclaims_training_data) without return types