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: Returns a ``forecast_step`` ahead rolling forecast plot.
  The plot consists one line for each valid. ``config_names``.
  If available, the corresponding actual values are also plotted.
  
  For a more customizable plot, see
  :func:`~greykite.common.viz.timeseries_plotting.plot_multivariate`
  
  Parameters
  ----------
  forecast_step : `int`
      Which forecast step to plot. A forecast step is an integer between 1 and the
      forecast horizon, inclusive, indicating the number of periods from train end date
      to the prediction date (# steps ahead).
  config_names : `list` [`str`], default None
      Which config results to plot. A list of config names.
      If None, uses all the available config keys.
  xlabel : `str` or None, default TIME_COL
      x-axis label.
  ylabel : `str` or None, default VALUE_COL
      y-axis label.
  title : `str` or None, default None
      Plot title. If None, default is based on ``forecast_step``.
  showlegend : `bool`, default True
      Whether to show the legend.
  
  Returns
  -------
  fig : `plotly.graph_objects.Figure`
      Interactive plotly graph.
      Plots multiple column(s) in ``self.forecasts`` against ``TIME_COL``.
  
      See `~greykite.common.viz.timeseries_plotting.plot_forecast_vs_actual`
      return value for how to plot the figure and add customization.
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code: def plot_forecasts_by_step(
      self,
      forecast_step: int,
      config_names: List = None,
      xlabel: str = TIME_COL,
      ylabel: str = VALUE_COL,
      title: str = None,
      showlegend: bool = True,
  ):
      """Returns a ``forecast_step`` ahead rolling forecast plot.
      The plot consists one line for each valid. ``config_names``.
      If available, the corresponding actual values are also plotted.
  
      For a more customizable plot, see
      :func:`~greykite.common.viz.timeseries_plotting.plot_multivariate`
  
      Parameters
      ----------
      forecast_step : `int`
          Which forecast step to plot. A forecast step is an integer between 1 and the
          forecast horizon, inclusive, indicating the number of periods from train end date
          to the prediction date (# steps ahead).
      config_names : `list` [`str`], default None
          Which config results to plot. A list of config names.
          If None, uses all the available config keys.
      xlabel : `str` or None, default TIME_COL
          x-axis label.
      ylabel : `str` or None, default VALUE_COL
          y-axis label.
      title : `str` or None, default None
          Plot title. If None, default is based on ``forecast_step``.
      showlegend : `bool`, default True
          Whether to show the legend.
  
      Returns
      -------
      fig : `plotly.graph_objects.Figure`
          Interactive plotly graph.
          Plots multiple column(s) in ``self.forecasts`` against ``TIME_COL``.
  
          See `~greykite.common.viz.timeseries_plotting.plot_forecast_vs_actual`
          return value for how to plot the figure and add customization.
      """
      if self.forecasts is None:
          self.extract_forecasts()
  
      if forecast_step > self.tscv.forecast_horizon:
          raise ValueError(
              f"`forecast_step` ({forecast_step}) must be less than or equal to "
              f"forecast horizon ({self.tscv.forecast_horizon})."
          )
  
      config_names = self.get_valid_config_names(config_names)
      y_cols = [TIME_COL, ACTUAL_COL] + [
          f"{config_name}_{PREDICTED_COL}"
          for config_name in config_names
      ]
  
      df = self.forecasts[
          self.forecasts[FORECAST_STEP_COL] == forecast_step
      ]
      df = df[y_cols]
  
      if title is None:
          title = f"{forecast_step}-step ahead rolling forecasts"
      fig = plot_multivariate(
          df=df,
          x_col=TIME_COL,
          y_col_style_dict="plotly",
          xlabel=xlabel,
          ylabel=ylabel,
          title=title,
          showlegend=showlegend,
      )
  
      return fig
  
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 5 for calling the BenchmarkForecastConfig obj's underlying member method code with example usage: obj.plot_forecasts_by_step(forecast_step, config_names, xlabel, ylabel, title, showlegend) and returns: fig