code: def test_prophet_simple():
      """Tests forecast_pipeline function with Prophet and default parameters"""
      data = generate_df_for_tests(freq="H", periods=24 * 10)
      df = data["df"][[TIME_COL, VALUE_COL]]
      hyperparameter_grid = {
          "estimator__weekly_seasonality": [True],
          "estimator__daily_seasonality": [True, False],
          "estimator__n_changepoints": [
              0
          ],  # to speed up test case, remove for better fit
          "estimator__uncertainty_samples": [
              10
          ],  # to speed up test case
      }
  
      # run pipeline with 1 CV fold, 2 parameter sets. Using default horizons
      with pytest.warns(Warning) as record:
          result = forecast_pipeline(
              df,
              score_func=EvaluationMetricEnum.MeanSquaredError.name,
              estimator=ProphetEstimator(),
              hyperparameter_grid=hyperparameter_grid,
              cv_min_train_periods=24 * 8,  # to speed up test case
              cv_report_metrics=[
                  EvaluationMetricEnum.MeanAbsoluteError.name
              ],
              cv_max_splits=1,
          )
          assert (
              "There is only one CV split" in record[0].message.args[0]
          )
          backtest_prophet_model = (
              result.grid_search.best_estimator_.steps[-1][-1].model
          )
          assert backtest_prophet_model.daily_seasonality
          assert backtest_prophet_model.weekly_seasonality
          assert backtest_prophet_model.n_changepoints == 0
          assert backtest_prophet_model.uncertainty_samples == 10
          check_forecast_pipeline_result(
              result,
              expected_grid_size=2,
              lower_bound_cv=0.0,  # default MAPE is non-negative
              score_func=EvaluationMetricEnum.MeanSquaredError.name,
              greater_is_better=False,
              cv_report_metrics_names=[
                  EvaluationMetricEnum.MeanAbsoluteError.get_metric_name(),
              ],
          )
          # Total data is 240 (24*10) rows. All of it is used for training;
          # 24 new rows created for testing based on default horizon setting. Test horizon & CV horizon is 24
          # Similary, Backtest training has 240-24 = 216 rows.
          assert result.forecast.df_train.shape == (240, 5)
          assert result.forecast.df_test.shape == (24, 5)
          assert result.backtest.df_train.shape == (216, 5)
          assert result.backtest.df_test.shape == (24, 5)
  
imports: import datetime
  import math
  import sys
  import warnings
  import numpy
  import pandas
  import pytest
  from scipy.stats import randint
  from sklearn.metrics import explained_variance_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.pipeline import Pipeline
  from greykite.common.constants import ACTUAL_COL
  from greykite.common.constants import ADJUSTMENT_DELTA_COL
  from greykite.common.constants import END_TIME_COL
  from greykite.common.constants import FRACTION_OUTSIDE_TOLERANCE
  from greykite.common.constants import METRIC_COL
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import QUANTILE_SUMMARY_COL
  from greykite.common.constants import START_TIME_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.constants import R2_null_model_score
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import add_finite_filter_to_scorer
  from greykite.common.evaluation import add_preaggregation_to_scorer
  from greykite.common.python_utils import assert_equal
  from greykite.common.python_utils import unique_elements_in_list
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.framework.constants import CV_REPORT_METRICS_ALL
  from greykite.framework.constants import FRACTION_OUTSIDE_TOLERANCE_NAME
  from greykite.framework.input.univariate_time_series import UnivariateTimeSeries
  from greykite.framework.pipeline.pipeline import forecast_pipeline
  from greykite.framework.templates.prophet_template import ProphetTemplate
  from greykite.framework.utils.framework_testing_utils import check_forecast_pipeline_result
  from greykite.framework.utils.framework_testing_utils import mock_pipeline
  from greykite.sklearn.estimator.null_model import DummyEstimator
  from greykite.sklearn.estimator.prophet_estimator import ProphetEstimator
  from greykite.sklearn.estimator.silverkite_estimator import SilverkiteEstimator
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.transform.column_selector import ColumnSelector
  from greykite.sklearn.transform.drop_degenerate_transformer import DropDegenerateTransformer
  from greykite.sklearn.transform.dtype_column_selector import DtypeColumnSelector
  from greykite.sklearn.transform.normalize_transformer import NormalizeTransformer
  from greykite.sklearn.transform.null_transformer import NullTransformer
  from greykite.sklearn.transform.pandas_feature_union import PandasFeatureUnion
  from greykite.sklearn.transform.zscore_outlier_transformer import ZscoreOutlierTransformer
label_desc: Use image node_id 14 for calling a global function with example usage: test_prophet_simple() without return types