code: def test_ets_missing(iclaims_training_data, estimator):
      df = iclaims_training_data
      missing_idx = np.array(
          [10, 20, 30, 40, 41, 42, 43, 44, df.shape[0] - 1]
      )
      df.loc[missing_idx, "claims"] = np.nan
  
      dlt = ETS(
          response_col="claims",
          date_col="week",
          seasonality=52,
          verbose=False,
          estimator=estimator,
      )
  
      dlt.fit(df)
      predicted_df = dlt.predict(df)
      if estimator == "stan-map":
          expected_columns = ["week", "prediction"]
      elif estimator == "stan-mcmc":
          expected_columns = [
              "week",
              "prediction_5",
              "prediction",
              "prediction_95",
          ]
  
      assert all(~np.isnan(predicted_df["prediction"]))
      assert predicted_df.columns.tolist() == expected_columns
      assert predicted_df.shape[0] == df.shape[0]
  
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 8 for calling a global function with example usage: test_ets_missing(iclaims_training_data, estimator) without return types