class_docstr: Defines the interface for the template class.
  
  Contains a single function that takes input data and forecast config,
  and returns parameters to call
  :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
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docstr: Converts forecast config to pipeline params.
  
  Takes input data and optional configuration parameters
  to customize the model. Returns a set of parameters to call
  :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
  
  Parameters
  ----------
  df : `pandas.DataFrame`
      Timeseries data to forecast.
      See :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline` for the format.
      This is the only required parameter.
  
      ``df`` should only contain time_col, value_col, and regressors.
      Every column in ``df`` besides time_col, value_col is assumed to be a regressor.
      Function does not allow prediction beyond the max available date with any regressor.
  config : :class:`~greykite.framework.templates.autogen.forecast_config.ForecastConfig` or None
      Forecast configuration, including metadata about ``df``,
      forecast horizon, prediction band coverage, evaluation setup,
      model components, and computation. See
      :class:`~greykite.framework.templates.autogen.forecast_config.ForecastConfig`.
      Can be initialized via::
  
          config = ForecastConfig(
              model_template=model_template,
              forecast_horizon=forecast_horizon,
              coverage=coverage,
              metadata_param=MetadataParam(...),
              evaluation_metric_param=EvaluationMetricParam(...),
              evaluation_period_param=EvaluationPeriodParam(...),
              model_components_param=ModelComponentsParam(...),
              computation_param=ComputationParam(...),
          )
  
  Returns
  -------
  forecast_parameters : `dict` [ `str`, `any` ]
      Parameters (keyword arguments) to call
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
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code: def apply_template_for_pipeline_params(
      self, df: pd.DataFrame, config: Optional[ForecastConfig] = None
  ) -> Dict:
      """Converts forecast config to pipeline params.
  
      Takes input data and optional configuration parameters
      to customize the model. Returns a set of parameters to call
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          Timeseries data to forecast.
          See :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline` for the format.
          This is the only required parameter.
  
          ``df`` should only contain time_col, value_col, and regressors.
          Every column in ``df`` besides time_col, value_col is assumed to be a regressor.
          Function does not allow prediction beyond the max available date with any regressor.
      config : :class:`~greykite.framework.templates.autogen.forecast_config.ForecastConfig` or None
          Forecast configuration, including metadata about ``df``,
          forecast horizon, prediction band coverage, evaluation setup,
          model components, and computation. See
          :class:`~greykite.framework.templates.autogen.forecast_config.ForecastConfig`.
          Can be initialized via::
  
              config = ForecastConfig(
                  model_template=model_template,
                  forecast_horizon=forecast_horizon,
                  coverage=coverage,
                  metadata_param=MetadataParam(...),
                  evaluation_metric_param=EvaluationMetricParam(...),
                  evaluation_period_param=EvaluationPeriodParam(...),
                  model_components_param=ModelComponentsParam(...),
                  computation_param=ComputationParam(...),
              )
  
      Returns
      -------
      forecast_parameters : `dict` [ `str`, `any` ]
          Parameters (keyword arguments) to call
          :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
      """
      pass
  
file_path: greykite/greykite/framework/templates/template_interface.py
imports: from abc import ABC
  from abc import abstractmethod
  from typing import Dict
  from typing import Optional
  import pandas
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
label_desc: Use image node_id 4 for calling the TemplateInterface obj's underlying member method code with example usage: obj.apply_template_for_pipeline_params(df, config) without return types