class_docstr: Class for benchmarking multiple ForecastConfig on a rolling window basis.
  
  Attributes
  ----------
  df : `pandas.DataFrame`
      Timeseries data to forecast.
      Contains columns [`time_col`, `value_col`], and optional regressor columns.
      Regressor columns should include future values for prediction.
  
  configs : `Dict` [`str`,  `ForecastConfig`]
      Dictionary of model configurations.
      A model configuration is a ``ForecastConfig``.
      See :class:`~greykite.framework.templates.autogen.forecast_config.ForecastConfig` for details on
      valid ``ForecastConfig``.
      Validity of the ``configs`` for benchmarking is checked via the ``validate`` method.
  
  tscv : `~greykite.sklearn.cross_validation.RollingTimeSeriesSplit`
      Cross-validation object that determines the rolling window evaluation.
      See :class:`~greykite.sklearn.cross_validation.RollingTimeSeriesSplit` for details.
      The ``forecast_horizon`` and ``periods_between_train_test`` parameters of ``configs`` are
      matched against that of ``tscv``. A ValueError is raised if there is a mismatch.
  
  forecaster : `~greykite.framework.templates.forecaster.Forecaster`
      Forecaster used to create the forecasts.
  
  is_run : bool, default False
      Indicator of whether the `run` method is executed.
      After executing `run`, this indicator is set to True.
      Some class methods like ``get_forecast`` requires ``is_run`` to be True
      to be executed.
  
  result : `dict`
      Stores the benchmarking results. Has the same keys as ``configs``.
  
  forecasts : `pandas.DataFrame`, default None
      Merged DataFrame of forecasts, upper and lower confidence interval for all
      input ``configs``. Also stores train end date and forecast step for each prediction.
docstr: Runs every config and stores the output of the
  :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
  This function runs only if the ``configs`` and ``tscv`` are jointly valid.
  
  Returns
  -------
  self : Returns self. Stores pipeline output of every config in ``self.result``.
docstr_tok: ["Runs","every","config","and","stores","the","output","of","the",":","func",":","`","~greykite.framework.pipeline.pipeline.forecast_pipeline","`",".","This","function","runs","only","if","the","``","configs","``","and","``","tscv","``","are","jointly","valid",".","Returns","--","--","--","-","self",":","Returns","self",".","Stores","pipeline","output","of","every","config","in","``","self.result","``","."]
code: def run(self):
      """Runs every config and stores the output of the
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
      This function runs only if the ``configs`` and ``tscv`` are jointly valid.
  
      Returns
      -------
      self : Returns self. Stores pipeline output of every config in ``self.result``.
      """
      self.validate()
      with tqdm(
          self.result.items(), ncols=800, leave=True
      ) as progress_bar:
          for config_name, config in progress_bar:
              # Description will be displayed on the left of progress bar
              progress_bar.set_description(
                  f"Benchmarking '{config_name}' "
              )
              rolling_evaluation = forecast_pipeline_rolling_evaluation(
                  pipeline_params=config["pipeline_params"],
                  tscv=self.tscv,
              )
              config["rolling_evaluation"] = rolling_evaluation
  
      self.is_run = True
  
imports: from typing import Dict
  from typing import List
  import numpy
  import pandas
  import plotly.graph_objects
  from plotly.colors import DEFAULT_PLOTLY_COLORS
  from tqdm.autonotebook import tqdm
  from greykite.common.constants import ACTUAL_COL
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import add_finite_filter_to_scorer
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import get_pattern_cols
  from greykite.common.viz.timeseries_plotting import plot_multivariate
  from greykite.common.viz.timeseries_plotting import plot_multivariate_grouped
  from greykite.framework.benchmark.benchmark_class_helper import forecast_pipeline_rolling_evaluation
  from greykite.framework.constants import FORECAST_STEP_COL
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.forecaster import Forecaster
  from greykite.sklearn.cross_validation import RollingTimeSeriesSplit
label_desc: Use image node_id 3 for calling the BenchmarkForecastConfig obj's underlying member method code with example usage: obj.run() without return types