code: def _process_foreground(
      self,
      foreground_series: Optional[
          Union[TimeSeries, Sequence[TimeSeries]]
      ] = None,
      foreground_past_covariates: Optional[
          Union[TimeSeries, Sequence[TimeSeries]]
      ] = None,
      foreground_future_covariates: Optional[
          Union[TimeSeries, Sequence[TimeSeries]]
      ] = None,
  ):
      return process_input(
          model=self.model,
          input_type="foreground",
          series=foreground_series,
          past_covariates=foreground_past_covariates,
          future_covariates=foreground_future_covariates,
          fallback_series=self.background_series,
          fallback_past_covariates=self.background_past_covariates,
          fallback_future_covariates=self.background_future_covariates,
          check_component_names=self.check_component_names,
          requires_input=self.requires_foreground,
          requires_covariates_encoding=self.requires_covariates_encoding,
          test_stationarity=self.test_stationarity,
      )
  
file_path: darts/darts/explainability/explainability.py
imports: from abc import ABC, abstractmethod
  from typing import Optional, Sequence, Tuple, Union
  from darts import TimeSeries
  from darts.explainability.explainability_result import _ExplainabilityResult
  from darts.explainability.utils import process_horizons_and_targets, process_input
  from darts.logging import get_logger, raise_log
  from darts.models.forecasting.forecasting_model import ForecastingModel
label_desc: Use image node_id 3 for calling the _ForecastingModelExplainer obj's underlying member method code with example usage: obj._process_foreground(foreground_series, foreground_past_covariates, foreground_future_covariates) and returns: process_input