code: def test_hyperparameter_grid(df):
      """Tests `forecast_pipeline` with various hyperparmeter_grid options.
  
      Parameters tested:
  
          - hyperparameter_grid=None, dict, list of dict
      """
      # hyperparameter_grid is None
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          result = forecast_pipeline(
              df,
              pipeline=get_dummy_pipeline(),
              hyperparameter_grid=None,
              hyperparameter_budget=1,
              forecast_horizon=10,
              test_horizon=10,
              cv_horizon=10,
          )
          # default values are used
          backtest_params = (
              result.grid_search.best_estimator_.get_params()
          )
          forecast_params = result.model.get_params()
          for params in [backtest_params, forecast_params]:
              assert params["estimator__constant"] is None
              assert params["estimator__quantile"] is None
              assert params["estimator__strategy"] == "mean"
  
      # hyperparameter_grid is a dict
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          hyperparameter_grid = {
              "input__response__outlier__z_cutoff": [
                  4.0
              ],  # pre-processing param
              "estimator__constant": [None],
              "estimator__quantile": [0.9],
              "estimator__strategy": ["quantile"],
          }
          result = forecast_pipeline(
              df,
              pipeline=get_dummy_pipeline(include_preprocessing=True),
              hyperparameter_grid=hyperparameter_grid,
              hyperparameter_budget=1,
              forecast_horizon=10,
              test_horizon=10,
              cv_horizon=10,
          )
  
          # tests if parameters are set from hyperparameter_grid
          backtest_params = (
              result.grid_search.best_estimator_.get_params()
          )
          forecast_params = result.model.get_params()
          for params in [backtest_params, forecast_params]:
              assert params["input__response__outlier__z_cutoff"] == 4.0
              assert params["estimator__constant"] is None
              assert params["estimator__quantile"] == 0.9
              assert params["estimator__strategy"] == "quantile"
  
      # hyperparameter_grid is a list of dict
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          hyperparameter_grid = [
              {"input__response__outlier__z_cutoff": [4.0]},
              {
                  "input__response__outlier__z_cutoff": [4.0],
                  "estimator__constant": [None],
                  "estimator__quantile": [0.9],
                  "estimator__strategy": ["quantile"],
              },
          ]
          result = forecast_pipeline(
              df,
              pipeline=get_dummy_pipeline(include_preprocessing=True),
              hyperparameter_grid=hyperparameter_grid,
              hyperparameter_budget=2,
              forecast_horizon=10,
              test_horizon=10,
              cv_horizon=10,
          )
  
          # tests if parameters are set from hyperparameter_grid
          backtest_params = (
              result.grid_search.best_estimator_.get_params()
          )
          forecast_params = result.model.get_params()
          for params in [backtest_params, forecast_params]:
              assert params["input__response__outlier__z_cutoff"] == 4.0
  
          assert result.grid_search.n_iter == 2
  
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 9 for calling a global function with example usage: test_hyperparameter_grid(df) without return types