code: def test_forecast_pipeline_coverage():
      """Forecast result with the following settings:
  
      * Hourly forecast with CV and backtest
      * 1 hyperparameter sets, 1 CV splits
      * All relevant `cv_report_metrics`.
      * Uses `SimpleSilverkiteEstimator`
      * Has prediction intervals
      """
      data = generate_df_for_tests(freq="H", periods=24 * 50)
      df = data["df"][[TIME_COL, VALUE_COL]]
      hyperparameter_grid = {
          "estimator__weekly_seasonality": [False],
          "estimator__daily_seasonality": [False],
      }
  
      def forecast_result(coverage):
          result = forecast_pipeline(
              df,
              score_func=EvaluationMetricEnum.MeanSquaredError.name,
              estimator=SimpleSilverkiteEstimator(),
              forecast_horizon=24 * 2,
              coverage=coverage,
              test_horizon=24,
              hyperparameter_grid=hyperparameter_grid,
              cv_horizon=24,
              cv_min_train_periods=24 * 30,
              cv_report_metrics=None,
              cv_max_splits=1,
              hyperparameter_budget=1,
              relative_error_tolerance=0.02,
              n_jobs=1,
          )
          return result
  
      result = forecast_result(coverage=0.9)
      check_forecast_pipeline_result(
          result,
          coverage=0.9,
          expected_grid_size=1,
          lower_bound_cv=0.0,
          score_func=EvaluationMetricEnum.MeanSquaredError.name,
          greater_is_better=False,
      )
  
      result = forecast_result(coverage=0.8)
      check_forecast_pipeline_result(
          result,
          coverage=0.8,
          expected_grid_size=1,
          lower_bound_cv=0.0,
          score_func=EvaluationMetricEnum.MeanSquaredError.name,
          greater_is_better=False,
      )
  
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 21 for calling a global function with example usage: test_forecast_pipeline_coverage() and returns: result