code: def _generate_train_idx(
      target, steps_ahead_start, steps_ahead_end
  ) -> SupportedIndex:
      """The returned index depends on the following cases:
  
      case 1
          (steps_ahead_start >= 0 and steps_ahead_end is None or <= 1)
          the complete index is within the target index; always True for all models except RegressionModels.
      case 2
          steps_ahead_start < 0: add indices before the target start time; only possible for RegressionModels
          where the minimum past lag is larger than input_chunk_length.
      case 3
          steps_ahead_end > 0: add indices after the target end time; only possible for RegressionModels
          where the maximum future lag is larger than output_chunk_length.
  
      Parameters
      ----------
      target
          the target series.
      steps_ahead_start
          how many steps ahead of target start time to begin the index.
      steps_ahead_end
          how many steps ahead of target end time to end the index.
      """
      # case 1
      if steps_ahead_start >= 0 and (
          steps_ahead_end is None or steps_ahead_end <= -1
      ):
          return target.time_index[steps_ahead_start:steps_ahead_end]
  
      # case 2
      idx_start = (
          generate_index(
              end=target.start_time() - target.freq,
              length=abs(steps_ahead_start),
              freq=target.freq,
          )
          if steps_ahead_start < 0
          else target.time_index.__class__([])
      )
  
      # if `steps_ahead_start >= 0` or `steps_ahead_end <= 0` we must extract a slice of the target series index
      center_start = (
          steps_ahead_start if steps_ahead_start >= 0 else None
      )
      center_end = (
          steps_ahead_end
          if steps_ahead_end is not None and steps_ahead_end <= 0
          else None
      )
      idx_center = target.time_index[center_start:center_end]
  
      # case 3
      idx_end = (
          generate_index(
              start=target.end_time() + target.freq,
              length=abs(steps_ahead_end),
              freq=target.freq,
          )
          if steps_ahead_end is not None and steps_ahead_end > 0
          else target.time_index.__class__([])
      )
  
      # concatenate start, center, and end index
      # note: pandas' union() returns type pd.Index(), so we construct index directly from index class
      return target.time_index.__class__(
          idx_start.union(idx_center).union(idx_end)
      )
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from darts import TimeSeries
  from darts.dataprocessing.transformers import FittableDataTransformer
  from darts.logging import get_logger, raise_if, raise_log
  from darts.utils.timeseries_generation import generate_index
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_train_idx(target, steps_ahead_start, steps_ahead_end) and returns: target, target