code: def _reconciliate_historical_time_indices(
      model,
      historical_forecasts_time_index_predict: TimeIndex,
      historical_forecasts_time_index_train: TimeIndex,
      series: TimeSeries,
      series_idx: int,
      retrain: Union[bool, int, Callable[..., bool]],
      train_length: Optional[int],
      show_warnings: bool,
  ) -> Tuple[TimeIndex, Optional[int]]:
      """Depending on the value of retrain, select which time indices will be used during the historical forecasts."""
      train_length_ = None
      if isinstance(retrain, Callable):
          # retain the longer time index, anything can happen
          if (
              historical_forecasts_time_index_train is not None
              and historical_forecasts_time_index_train[0]
              < historical_forecasts_time_index_predict[0]
          ):
              historical_forecasts_time_index = (
                  historical_forecasts_time_index_train
              )
          else:
              historical_forecasts_time_index = (
                  historical_forecasts_time_index_predict
              )
      elif retrain:
          historical_forecasts_time_index = (
              historical_forecasts_time_index_train
          )
      else:
          historical_forecasts_time_index = (
              historical_forecasts_time_index_predict
          )
  
      # compute the maximum forecasts time index assuming that `start=None`
      if retrain or (not model._fit_called):
          if train_length and train_length <= len(series):
              train_length_ = train_length
              # we have to start later for larger `train_length`
              step_ahead = max(
                  train_length
                  - model._training_sample_time_index_length,
                  0,
              )
              if step_ahead:
                  historical_forecasts_time_index = (
                      historical_forecasts_time_index[0]
                      + step_ahead * series.freq,
                      historical_forecasts_time_index[-1],
                  )
  
          # if not we start training right away; some models (sklearn) require more than 1
          # training samples, so we start after the first trainable point.
          else:
              if (
                  train_length
                  and train_length > len(series)
                  and show_warnings
              ):
                  logger.warning(
                      f"`train_length` is larger than the length of series at index: {series_idx}. "
                      f"Ignoring `train_length` and using default behavior where all available time steps up "
                      f"until the end of the expanding training set. "
                      f"To hide these warnings, set `show_warnings=False`."
                  )
  
              train_length_ = None
              if model.min_train_samples > 1:
                  historical_forecasts_time_index = (
                      historical_forecasts_time_index[0]
                      + (model.min_train_samples - 1) * series.freq,
                      historical_forecasts_time_index[1],
                  )
  
      return historical_forecasts_time_index, train_length_
  
imports: from types import SimpleNamespace
  from typing import Any, Callable, Dict, Optional, Sequence, Set, Tuple, Union
  import inspect
  import numpy
  import pandas
  from numpy.typing import ArrayLike
  from darts.logging import get_logger, raise_if_not, raise_log
  from darts.timeseries import TimeSeries
  from darts.utils.timeseries_generation import generate_index
  from darts.utils.utils import series2seq
label_desc: Use image node_id 9 for calling a global function with example usage: _reconciliate_historical_time_indices(model, historical_forecasts_time_index_predict, historical_forecasts_time_index_train, series, series_idx, retrain, train_length, show_warnings) and returns: historical_forecasts_time_index,  train_length_