code: def test_dlt_map_univariate(make_weekly_data):
      train_df, test_df, coef = make_weekly_data
  
      dlt = DLT(
          response_col="response",
          date_col="week",
          seasonality=52,
          num_warmup=50,
          verbose=False,
          estimator="stan-map",
      )
  
      dlt.fit(train_df)
  
      init_values = dlt._model.get_init_values()
      assert init_values["init_sea"].shape == (51,)
  
      predict_df = dlt.predict(test_df)
  
      expected_columns = ["week", "prediction"]
      expected_shape = (51, len(expected_columns))
      expected_num_parameters = (
          len(dlt._model.get_model_param_names()) + 1
      )
  
      assert predict_df.shape == expected_shape
      assert predict_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 4 for calling a global function with example usage: test_dlt_map_univariate(make_weekly_data) without return types