code: def _model_encoder_settings(
      self,
  ) -> Tuple[
      int, int, bool, bool, Optional[List[int]], Optional[List[int]]
  ]:
      target_lags = self.lags.get("target", [0])
      lags_past_covariates = self.lags.get("past", None)
      if lags_past_covariates is not None:
          lags_past_covariates = [
              min(lags_past_covariates)
              - int(not self.multi_models)
              * (self.output_chunk_length - 1),
              max(lags_past_covariates),
          ]
      lags_future_covariates = self.lags.get("future", None)
      if lags_future_covariates is not None:
          lags_future_covariates = [
              min(lags_future_covariates)
              - int(not self.multi_models)
              * (self.output_chunk_length - 1),
              max(lags_future_covariates),
          ]
      return (
          abs(min(target_lags)),
          self.output_chunk_length,
          lags_past_covariates is not None,
          lags_future_covariates is not None,
          lags_past_covariates,
          lags_future_covariates,
      )
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.models.forecasting.forecasting_model import GlobalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.data.tabularization import add_static_covariates_to_lagged_data, create_lagged_component_names, create_lagged_training_data
  from darts.utils.historical_forecasts import _check_optimizable_historical_forecasts_global_models, _optimized_historical_forecasts_all_points, _optimized_historical_forecasts_last_points_only, _process_historical_forecast_input
  from darts.utils.multioutput import MultiOutputRegressor
  from darts.utils.utils import _check_quantiles, get_single_series, seq2series, series2seq
label_desc: Use image node_id 4 for calling the RegressionModel obj's underlying member method code with example usage: obj._model_encoder_settings() and returns: abs,  self,  unknown,  unknown,  lags_past_covariates,  lags_future_covariates