class_docstr: Generates index for past covariates on train and inference datasets
class_docstr_tok: ["Generates","index","for","past","covariates","on","train","and","inference","datasets"]
code: def generate_inference_idx(
      self,
      n: int,
      target: TimeSeries,
      covariates: Optional[TimeSeries] = None,
  ) -> Tuple[SupportedIndex, pd.Timestamp]:
      super().generate_inference_idx(n, target, covariates)
  
      # for prediction (`n` is given) with past covariates the returned index depends on the following cases:
      # case 0
      #     user supplied covariates: simply return the covariate time index; guarantees that an exception is
      #     raised if user supplied insufficient covariates.
      # case 1
      #     only input_chunk_length and output_chunk_length are given: we need to generate a time index that starts
      #     `input_chunk_length - 1` before the end of `target` and ends `max(0, n - output_chunk_length)` after the
      #     end of `target`; always True for all models except RegressionModels.
      # case 2
      #     covariate lags were given (shift_start <= 0 and shift_end <= 0): we need to generate a time index that
      #     starts `-shift_start` before the end of `target` and has a length of
      #     `shift_steps + max(0, n - output_chunk_length)`, where `shift_steps` is the number of time steps between
      #     `shift_start` and `shift_end`; can only be True for RegressionModels.
  
      target_end = target.end_time()
      if covariates is not None:  # case 0
          return covariates.time_index, target_end
  
      if self.shift_start is None or self.shift_end is None:  # case 1
          steps_back_end = self.input_chunk_length - 1
          n_steps = (
              steps_back_end + 1 + max(0, n - self.output_chunk_length)
          )
      else:  # case 2
          steps_back_end = -self.shift_start
          shift_steps = self.shift_end - self.shift_start + 1
          n_steps = shift_steps + max(0, n - self.output_chunk_length)
  
      return (
          generate_index(
              start=target.end_time() - target.freq * steps_back_end,
              length=n_steps,
              freq=target.freq,
          ),
          target_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 2 for calling the PastCovariatesIndexGenerator obj's underlying member method code with example usage: obj.generate_inference_idx(n, target, covariates) and returns: generate_index,  target_end, covariates,  target_end