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: Validate ``config_names`` against keys of ``configs``.
  Raises a ValueError in case of a mismatch.
  
  Parameters
  ----------
   config_names : `list` [`str`], default None
      Which config results to plot. A list of config names.
      If None, uses all the available config keys.
  
  Returns
  -------
  config_names : `list`
      List of valid config names.
docstr_tok: ["Validate","``","config_names","``","against","keys","of","``","configs","``",".","Raises","a","ValueError","in","case","of","a","mismatch",".","Parameters","--","--","--","--","--","config_names",":","`","list","`","[","`","str","`","]",",","default","None","Which","config","results","to","plot",".","A","list","of","config","names",".","If","None",",","uses","all","the","available","config","keys",".","Returns","--","--","--","-","config_names",":","`","list","`","List","of","valid","config","names","."]
code: def get_valid_config_names(self, config_names: List = None):
      """Validate ``config_names`` against keys of ``configs``.
      Raises a ValueError in case of a mismatch.
  
      Parameters
      ----------
       config_names : `list` [`str`], default None
          Which config results to plot. A list of config names.
          If None, uses all the available config keys.
  
      Returns
      -------
      config_names : `list`
          List of valid config names.
      """
      available_config_names = list(self.configs.keys())
      if config_names is None:
          config_names = available_config_names
      else:
          missing_config_names = set(config_names) - set(
              available_config_names
          )
          if len(missing_config_names) > 0:
              raise ValueError(
                  f"The following config keys are missing: {missing_config_names}."
              )
  
      return config_names
  
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 13 for calling the BenchmarkForecastConfig obj's underlying member method code with example usage: obj.get_valid_config_names(config_names) and returns: config_names