code: def get_auto_growth(
      df: pd.DataFrame,
      time_col: str,
      value_col: str,
      forecast_horizon: int,
      changepoints_dict_override: Optional[dict] = None,
  ):
      """Automatically gets the parameters for growth.
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          The input time series.
      time_col : `str`
          The column name for timestamps in ``df``.
      value_col : `str`
          The column name for values in ``df``.
      forecast_horizon : `int`
          The forecast horizon.
      changepoints_dict_override : `dict` or None, default None
          The changepoints configuration.
          If the following customized dates related keys are given,
          they will be copied to the final configuration:
  
              - "dates"
              - "combine_changepoint_min_distance"
              - "keep_detected"
  
      Returns
      -------
      result : `dict`
          A dictionary with the following keys:
  
              - "growth_term": `str`
                The growth term.
              - "changepoints_dict": `dict`
                The changepoints configuration used for automatic changepoint detection.
  
      """
      # Gets the growth and changepoints configurations.
      growth_term = GrowthColEnum.linear.name
      trend_changepoints_param = (
          generate_trend_changepoint_detection_params(
              df=df,
              forecast_horizon=forecast_horizon,
              time_col=time_col,
              value_col=value_col,
          )
      )
      trend_changepoints_param["method"] = "auto"
      # Inherits the user specified dates.
      if (
          changepoints_dict_override is not None
          and changepoints_dict_override.get("method", None) != "custom"
      ):
          for key in [
              "dates",
              "combine_changepoint_min_distance",
              "keep_detected",
          ]:
              if key in changepoints_dict_override:
                  trend_changepoints_param[
                      key
                  ] = changepoints_dict_override[key]
      return dict(
          growth_term=growth_term,
          changepoints_dict=trend_changepoints_param,
      )
  
imports: import inspect
  from datetime import timedelta
  from typing import Dict
  from typing import List
  from typing import Optional
  import numpy
  import pandas
  from greykite.algo.changepoint.adalasso.auto_changepoint_params import generate_trend_changepoint_detection_params
  from greykite.algo.common.holiday_grouper import HolidayGrouper
  from greykite.algo.common.seasonality_inferrer import SeasonalityInferConfig
  from greykite.algo.common.seasonality_inferrer import SeasonalityInferrer
  from greykite.algo.common.seasonality_inferrer import TrendAdjustMethodEnum
  from greykite.algo.forecast.silverkite.forecast_simple_silverkite_helper import split_events_into_dictionaries
  from greykite.common import constants
  from greykite.common.constants import GrowthColEnum
  from greykite.common.constants import TimeFeaturesEnum
  from greykite.common.features.timeseries_features import add_event_window_multi
  from greykite.common.features.timeseries_features import get_holidays
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import update_dictionary
  from greykite.common.time_properties import describe_timeseries
label_desc: Use image node_id 2 for calling a global function with example usage: get_auto_growth(df, time_col, value_col, forecast_horizon, changepoints_dict_override) and returns: dict