code: def test_incomplete_fit_df_warning(params, hourly_data_with_reg):
      model = MultistageForecastEstimator(**params)
      model.model_configs[0].estimator_params["regressor_cols"] = [
          "regressor"
      ]
      with LogCapture(cst.LOGGER_NAME) as log_capture:
          model.fit(
              X=hourly_data_with_reg.iloc[:-1]
          )  # The last period is incomplete.
          log_capture.check(
              (
                  cst.LOGGER_NAME,
                  "WARNING",
                  "There are incomplete periods in `fit_df`, thus the regressor values are "
                  "biased after aggregation.",
              )
          )
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from testfixtures import LogCapture
  import greykite.common.constants
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.sklearn.estimator.lag_based_estimator import LagBasedEstimator
  from greykite.sklearn.estimator.multistage_forecast_estimator import MultistageForecastEstimator
  from greykite.sklearn.estimator.multistage_forecast_estimator import MultistageForecastModelConfig
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.uncertainty.uncertainty_methods import UncertaintyMethodEnum
label_desc: Use image node_id 20 for calling a global function with example usage: test_incomplete_fit_df_warning(params, hourly_data_with_reg) without return types