code: def __getitem__(
      self, idx
  ) -> Tuple[
      np.ndarray,
      Optional[np.ndarray],
      Optional[np.ndarray],
      Optional[np.ndarray],
      Optional[np.ndarray],
      TimeSeries,
      Union[pd.Timestamp, int],
  ]:
      (
          past_target,
          past_covariate,
          future_past_covariate,
          static_covariate,
          ts_target,
          pred_point,
      ) = self.ds_past[idx]
      _, future_covariate, _, _, _ = self.ds_future[idx]
      return (
          past_target,
          past_covariate,
          future_covariate,
          future_past_covariate,
          static_covariate,
          ts_target,
          pred_point,
      )
  
imports: import bisect
  from abc import ABC, abstractmethod
  from typing import Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from torch.utils.data import Dataset
  from darts import TimeSeries
  from darts.logging import get_logger, raise_log
  from darts.utils.historical_forecasts.utils import _process_predict_start_points_bounds
  from .utils import CovariateType
label_desc: Use image node_id 3 for calling the SplitCovariatesInferenceDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: past_target,  past_covariate,  future_covariate,  future_past_covariate,  static_covariate,  ts_target,  pred_point