class_docstr: A template for :class: `~greykite.sklearn.estimator.lag_based_estimator.LagBasedEstimator`.
      
class_docstr_tok: ["A","template","for",":","class",":","`","~greykite.sklearn.estimator.lag_based_estimator.LagBasedEstimator","`","."]
docstr: Explicitly calls the method in
  `~greykite.framework.templates.base_template.BaseTemplate`
  to make use of the decorator in this class.
  
  Parameters
  ----------
  df : `pandas.DataFrame`
      The time series dataframe with ``time_col`` and ``value_col`` and optional regressor columns.
  config : `~greykite.framework.templates.autogen.forecast_config.ForecastConfig`.
      The `ForecastConfig` class that includes model training parameters.
  
  Returns
  -------
  pipeline_parameters : `dict`
      The pipeline parameters consumable by
      `~greykite.framework.pipeline.pipeline.forecast_pipeline`.
docstr_tok: ["Explicitly","calls","the","method","in","`","~greykite.framework.templates.base_template.BaseTemplate","`","to","make","use","of","the","decorator","in","this","class",".","Parameters","--","--","--","--","--","df",":","`","pandas.DataFrame","`","The","time","series","dataframe","with","``","time_col","``","and","``","value_col","``","and","optional","regressor","columns",".","config",":","`","~greykite.framework.templates.autogen.forecast_config.ForecastConfig","`",".","The","`","ForecastConfig","`","class","that","includes","model","training","parameters",".","Returns","--","--","--","-","pipeline_parameters",":","`","dict","`","The","pipeline","parameters","consumable","by","`","~greykite.framework.pipeline.pipeline.forecast_pipeline","`","."]
code: def apply_template_for_pipeline_params(
      self, df: pd.DataFrame, config: Optional[ForecastConfig] = None
  ) -> Dict:
      """Explicitly calls the method in
      `~greykite.framework.templates.base_template.BaseTemplate`
      to make use of the decorator in this class.
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          The time series dataframe with ``time_col`` and ``value_col`` and optional regressor columns.
      config : `~greykite.framework.templates.autogen.forecast_config.ForecastConfig`.
          The `ForecastConfig` class that includes model training parameters.
  
      Returns
      -------
      pipeline_parameters : `dict`
          The pipeline parameters consumable by
          `~greykite.framework.pipeline.pipeline.forecast_pipeline`.
      """
      return super().apply_template_for_pipeline_params(
          df=df, config=config
      )
  
imports: import functools
  from typing import Dict
  from typing import Optional
  import pandas
  from greykite.common.aggregation_function_enum import AggregationFunctionEnum
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import dictionaries_values_to_lists
  from greykite.common.python_utils import update_dictionaries
  from greykite.common.python_utils import update_dictionary
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import ModelComponentsParam
  from greykite.framework.templates.base_template import BaseTemplate
  from greykite.sklearn.estimator.base_forecast_estimator import BaseForecastEstimator
  from greykite.sklearn.estimator.lag_based_estimator import LagBasedEstimator
  from greykite.sklearn.estimator.lag_based_estimator import LagUnitEnum
label_desc: Use image node_id 8 for calling the LagBasedTemplate obj's underlying member method code with example usage: obj.apply_template_for_pipeline_params(df, config) and returns: super