code: def test_ets_aggregated_seasonal_fit(make_weekly_data, point_method):
      train_df, test_df, coef = make_weekly_data
  
      ets = ETS(
          response_col="response",
          date_col="week",
          seasonality=52,
          num_warmup=50,
          num_sample=50,
          verbose=False,
          estimator="stan-mcmc",
          n_bootstrap_draws=1e4,
      )
      ets.fit(train_df, point_method=point_method)
  
      init_values = ets._model.get_init_values()
      assert init_values["init_sea"].shape == (51,)
  
      predict_df = ets.predict(test_df, decompose=False)
  
      expected_columns = [
          "week",
          "prediction_5",
          "prediction",
          "prediction_95",
      ]
      expected_shape = (51, len(expected_columns))
      expected_num_parameters = (
          len(ets._model.get_model_param_names()) + 1
      )
  
      assert predict_df.shape == expected_shape
      assert predict_df.columns.tolist() == expected_columns
      assert len(ets._posterior_samples) == expected_num_parameters
  
file_path: orbit/tests/orbit/models/test_ets.py
imports: import pytest
  import numpy
  from copy import copy
  from orbit.models import ETS
  from orbit.constants.constants import PredictionKeys
label_desc: Use image node_id 3 for calling a global function with example usage: test_ets_aggregated_seasonal_fit(make_weekly_data, point_method) without return types