code: def __getitem__(
      self, idx
  ) -> Tuple[
      np.ndarray,
      Optional[np.ndarray],
      Optional[np.ndarray],
      Optional[np.ndarray],
  ]:
      # determine the index of the time series.
      target_idx = idx // self.max_samples_per_ts
      target_series = self.target_series[target_idx]
      target_vals = target_series.random_component_values(copy=False)
  
      # determine the actual number of possible samples in this time series
      n_samples_in_ts = len(target_vals) - self.size_of_both_chunks + 1
  
      raise_if_not(
          n_samples_in_ts >= 1,
          "The dataset contains some time series that are too short to contain "
          "`max(self.input_chunk_length, self.shift + self.output_chunk_length)` "
          "({}-th series)".format(target_idx),
      )
  
      # determine the index at the end of the output chunk
      # it is originally in [0, self.max_samples_per_ts), so we use a modulo to have it in [0, n_samples_in_ts)
      end_of_output_idx = (
          len(target_series)
          - (idx - (target_idx * self.max_samples_per_ts))
          % n_samples_in_ts
      )
  
      # optionally, load covariates
      covariate_series = (
          self.covariates[target_idx]
          if self.covariates is not None
          else None
      )
  
      main_covariate_type = CovariateType.NONE
      if self.covariates is not None:
          main_covariate_type = (
              CovariateType.FUTURE
              if self.shift_covariates
              else CovariateType.PAST
          )
  
      # get all indices for the current sample
      (
          past_start,
          past_end,
          future_start,
          future_end,
          covariate_start,
          covariate_end,
      ) = self._memory_indexer(
          target_idx=target_idx,
          target_series=target_series,
          shift=self.shift,
          input_chunk_length=self.input_chunk_length,
          output_chunk_length=self.output_chunk_length,
          end_of_output_idx=end_of_output_idx,
          covariate_series=covariate_series,
          covariate_type=main_covariate_type,
      )
  
      # extract sample target
      future_target = target_vals[future_start:future_end]
      past_target = target_vals[past_start:past_end]
  
      # optionally, extract sample covariates
      covariate = None
      if self.covariates is not None:
          raise_if_not(
              covariate_end <= len(covariate_series),
              f"The dataset contains {main_covariate_type.value} covariates "
              f"that don't extend far enough into the future. ({idx}-th sample)",
          )
  
          covariate = covariate_series.random_component_values(
              copy=False
          )[covariate_start:covariate_end]
  
          raise_if_not(
              len(covariate)
              == (
                  self.output_chunk_length
                  if self.shift_covariates
                  else self.input_chunk_length
              ),
              f"The dataset contains {main_covariate_type.value} covariates "
              f"whose time axis doesn't allow to obtain the input (or output) chunk relative to the "
              f"target series.",
          )
  
      if self.use_static_covariates:
          static_covariate = target_series.static_covariates_values(
              copy=False
          )
      else:
          static_covariate = None
      return past_target, covariate, static_covariate, future_target
  
imports: from typing import Optional, Sequence, Tuple, Union
  import numpy
  from darts import TimeSeries
  from darts.logging import raise_if_not
  from .training_dataset import DualCovariatesTrainingDataset, FutureCovariatesTrainingDataset, MixedCovariatesTrainingDataset, PastCovariatesTrainingDataset, SplitCovariatesTrainingDataset, TrainingDataset
  from .utils import CovariateType
label_desc: Use image node_id 3 for calling the GenericShiftedDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: past_target,  covariate,  static_covariate,  future_target