code: def get_dummy_pipeline(
      include_preprocessing=False,
      regressor_cols=None,
      lagged_regressor_cols=None,
  ):
      """Returns a ``pipeline`` argument to ``forecast_pipeline``
      that uses ``DummyEstimator`` to make it easy to unit test
      ``forecast_pipeline``.
  
      Parameters
      ----------
      include_preprocessing : `bool`, default False
          If True, includes preprocessing steps.
      regressor_cols : `list` [`str`] or None, default None
          Names of regressors in ``df`` passed to ``forecast_pipeline``.
          Only used if ``include_preprocessing=True``.
      lagged_regressor_cols : `list` [`str`] or None, default None
          Names of lagged regressor columns in ``df`` passed to ``forecast_pipeline``.
          Only used if ``include_preprocessing=True``.
  
      Returns
      -------
      pipeline : `sklearn.pipeline.Pipeline`
          sklearn Pipeline for univariate forecasting.
      """
      if regressor_cols is None:
          regressor_cols = []
      if lagged_regressor_cols is None:
          lagged_regressor_cols = []
      all_reg_cols = unique_elements_in_list(
          regressor_cols + lagged_regressor_cols
      )
      steps = []
      if include_preprocessing:
          steps += [
              (
                  "input",
                  PandasFeatureUnion(
                      [
                          (
                              "date",
                              Pipeline(
                                  [
                                      (
                                          "select_date",
                                          ColumnSelector([TIME_COL]),
                                      )  # leaves time column unmodified
                                  ]
                              ),
                          ),
                          (
                              "response",
                              Pipeline(
                                  [  # applies outlier and null transformation to value column
                                      (
                                          "select_val",
                                          ColumnSelector([VALUE_COL]),
                                      ),
                                      (
                                          "outlier",
                                          ZscoreOutlierTransformer(),
                                      ),
                                      ("null", NullTransformer()),
                                  ]
                              ),
                          ),
                          (
                              "regressors_numeric",
                              Pipeline(
                                  [
                                      (
                                          "select_reg",
                                          ColumnSelector(all_reg_cols),
                                      ),
                                      (
                                          "select_reg_numeric",
                                          DtypeColumnSelector(
                                              include="number"
                                          ),
                                      ),
                                      (
                                          "outlier",
                                          ZscoreOutlierTransformer(),
                                      ),
                                      (
                                          "normalize",
                                          NormalizeTransformer(),
                                      ),  # no normalization by default
                                      ("null", NullTransformer()),
                                  ]
                              ),
                          ),
                          (
                              "regressors_other",
                              Pipeline(
                                  [
                                      (
                                          "select_reg",
                                          ColumnSelector(all_reg_cols),
                                      ),
                                      (
                                          "select_reg_non_numeric",
                                          DtypeColumnSelector(
                                              exclude="number"
                                          ),
                                      ),
                                  ]
                              ),
                          ),
                      ]
                  ),
              ),
              (
                  "degenerate",
                  DropDegenerateTransformer(),
              ),  # default `drop_degenerate=False`
          ]
      steps += [("estimator", DummyEstimator())]  # predicts a constant
      return Pipeline(steps)
  
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 3 for calling a global function with example usage: get_dummy_pipeline(include_preprocessing, regressor_cols, lagged_regressor_cols) and returns: Pipeline