code: def test_uncertainty_with_nonstandard_cols(daily_data):
      model = SimpleSilverkiteEstimator(coverage=0.95)
      df = daily_data["df"].rename(
          columns={cst.TIME_COL: "t", cst.VALUE_COL: "z"}
      )
      model.fit(df, time_col="t", value_col="z")
      pred_df = model.predict(df)
      assert cst.PREDICTED_LOWER_COL in pred_df
      assert cst.PREDICTED_UPPER_COL in pred_df
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from pandas.testing import assert_frame_equal
  from sklearn.linear_model import SGDRegressor
  from sklearn.metrics import mean_squared_error
  import greykite.common.constants
  from greykite.algo.forecast.silverkite.forecast_simple_silverkite import SimpleSilverkiteForecast
  from greykite.common.constants import EVENT_DF_DATE_COL
  from greykite.common.constants import EVENT_DF_LABEL_COL
  from greykite.common.data_loader import DataLoader
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import daily_data_reg
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.estimator.testing_utils import params_component_breakdowns
label_desc: Use image node_id 17 for calling a global function with example usage: test_uncertainty_with_nonstandard_cols(daily_data) without return types