code: def run_dummy_grid_search(hyperparameter_grid, n_jobs=1, **kwargs):
      """Runs a pandas.DataFrame through hyperparameter_grid search
      with custom CV splits on a simple dataset to show that
      all the pieces fit together.
  
      Parameters
      ----------
      hyperparameter_grid : `dict` or `list` [`dict`]
          Passed to ``get_hyperparameter_searcher``.
          Should be compatible with DummyEstimator
      n_jobs : `int` or None, default=-1
          Passed to ``get_hyperparameter_searcher``
      kwargs : additional parameters
          Passed to ``get_hyperparameter_searcher``
  
      Returns
      -------
      grid_search : `~sklearn.model_selection.RandomizedSearchCV`
          Grid search output (fitted RandomizedSearchCV object).
      """
      # dummy dataset, model, and CV splitter
      periods = 10
      X = pd.DataFrame(
          {
              cst.TIME_COL: pd.date_range(
                  "2018-01-01", periods=periods, freq="D"
              ),
              cst.VALUE_COL: np.arange(1, periods + 1),
          }
      )
      model = DummyEstimator()
      cv = RollingTimeSeriesSplit(forecast_horizon=3)  # 1 CV split
  
      # requested grid searcher
      grid_search = get_hyperparameter_searcher(
          hyperparameter_grid=hyperparameter_grid,
          model=model,
          cv=cv,
          n_jobs=n_jobs,
          **kwargs,
      )
  
      grid_search.fit(
          X, X[cst.VALUE_COL]
      )  # need to pass in y to evaluate score() function
      return grid_search
  
imports: import sys
  import warnings
  from functools import partial
  import numpy
  import pandas
  import pytest
  from scipy.stats import randint
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import median_absolute_error
  from sklearn.pipeline import Pipeline
  from testfixtures import LogCapture
  from greykite.common import constants
  from greykite.common.constants import FRACTION_OUTSIDE_TOLERANCE
  from greykite.common.constants import LOGGER_NAME
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import add_preaggregation_to_scorer
  from greykite.common.evaluation import fraction_outside_tolerance
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import assert_eval_function_equal
  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 CUSTOM_SCORE_FUNC_NAME
  from greykite.framework.constants import CV_REPORT_METRICS_ALL
  from greykite.framework.constants import FRACTION_OUTSIDE_TOLERANCE_NAME
  from greykite.framework.pipeline.utils import get_basic_pipeline
  from greykite.framework.pipeline.utils import get_best_index
  from greykite.framework.pipeline.utils import get_default_time_parameters
  from greykite.framework.pipeline.utils import get_forecast
  from greykite.framework.pipeline.utils import get_hyperparameter_searcher
  from greykite.framework.pipeline.utils import get_score_func_with_aggregation
  from greykite.framework.pipeline.utils import get_scoring_and_refit
  from greykite.framework.utils.framework_testing_utils import assert_proper_grid_search
  from greykite.framework.utils.framework_testing_utils import assert_refit
  from greykite.framework.utils.framework_testing_utils import assert_scoring
  from greykite.sklearn.cross_validation import RollingTimeSeriesSplit
  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.transform.column_selector import ColumnSelector
label_desc: Use image node_id 10 for calling a global function with example usage: run_dummy_grid_search(hyperparameter_grid, n_jobs) and returns: grid_search