code: def get_prediction_index(
      start,
      end,
      nobs,
      base_index,
      index=None,
      silent=False,
      index_none=False,
      index_generated=None,
      data=None,
  ) -> tuple[int, int, int, Index | None]:
      """
      Get the location of a specific key in an index or model row labels
  
      Parameters
      ----------
      start : label
          The key at which to start prediction. Depending on the underlying
          model's index, may be an integer, a date (string, datetime object,
          pd.Timestamp, or pd.Period object), or some other object in the
          model's row labels.
      end : label
          The key at which to end prediction (note that this key will be
          *included* in prediction). Depending on the underlying
          model's index, may be an integer, a date (string, datetime object,
          pd.Timestamp, or pd.Period object), or some other object in the
          model's row labels.
      nobs : int
      base_index : pd.Index
  
      index : pd.Index, optional
          Optionally an index to associate the predicted results to. If None,
          an attempt is made to create an index for the predicted results
          from the model's index or model's row labels.
      silent : bool, optional
          Argument to silence warnings.
  
      Returns
      -------
      start : int
          The index / observation location at which to begin prediction.
      end : int
          The index / observation location at which to end in-sample
          prediction. The maximum value for this is nobs-1.
      out_of_sample : int
          The number of observations to forecast after the end of the sample.
      prediction_index : pd.Index or None
          The index associated with the prediction results. This index covers
          the range [start, end + out_of_sample]. If the model has no given
          index and no given row labels (i.e. endog/exog is not Pandas), then
          this will be None.
  
      Notes
      -----
      The arguments `start` and `end` behave differently, depending on if
      they are integer or not. If either is an integer, then it is assumed
      to refer to a *location* in the index, not to an index value. On the
      other hand, if it is a date string or some other type of object, then
      it is assumed to refer to an index *value*. In all cases, the returned
      `start` and `end` values refer to index *locations* (so in the former
      case, the given location is validated and returned whereas in the
      latter case a location is found that corresponds to the given index
      value).
  
      This difference in behavior is necessary to support `RangeIndex`. This
      is because integers for a RangeIndex could refer either to index values
      or to index locations in an ambiguous way (while for `NumericIndex`,
      since we have required them to be full indexes, there is no ambiguity).
      """
  
      # Convert index keys (start, end) to index locations and get associated
      # indexes.
      try:
          start, _, start_oos = get_index_label_loc(
              start, base_index, data.row_labels
          )
      except KeyError:
          raise KeyError(
              "The `start` argument could not be matched to a"
              " location related to the index of the data."
          )
      if end is None:
          end = max(start, len(base_index) - 1)
      try:
          end, end_index, end_oos = get_index_label_loc(
              end, base_index, data.row_labels
          )
      except KeyError:
          raise KeyError(
              "The `end` argument could not be matched to a"
              " location related to the index of the data."
          )
  
      # Handle slices (if the given index keys cover more than one date)
      if isinstance(start, slice):
          start = start.start
      if isinstance(end, slice):
          end = end.stop - 1
  
      # Get the actual index for the prediction
      prediction_index = end_index[start:]
  
      # Validate prediction options
      if end < start:
          raise ValueError("Prediction must have `end` after `start`.")
  
      # Handle custom prediction index
      # First, if we were given an index, check that it's the right size and
      # use it if so
      if index is not None:
          if not len(prediction_index) == len(index):
              raise ValueError(
                  "Invalid `index` provided in prediction."
                  " Must have length consistent with `start`"
                  " and `end` arguments."
              )
          # But if we weren't given Pandas input, this index will not be
          # used because the data will not be wrapped; in that case, issue
          # a warning
          if not isinstance(data, PandasData) and not silent:
              warnings.warn(
                  "Because the model data (`endog`, `exog`) were"
                  " not given as Pandas objects, the prediction"
                  " output will be Numpy arrays, and the given"
                  " `index` argument will only be used"
                  " internally.",
                  ValueWarning,
                  stacklevel=2,
              )
          prediction_index = Index(index)
      # Now, if we *do not* have a supported index, but we were given some
      # kind of index...
      elif index_generated and not index_none:
          # If we are in sample, and have row labels, use them
          if data.row_labels is not None and not (start_oos or end_oos):
              prediction_index = data.row_labels[start : end + 1]
          # Otherwise, warn the user that they will get an NumericIndex
          else:
              warnings.warn(
                  "No supported index is available. In the next"
                  " version, calling this method in a model"
                  " without a supported index will result in an"
                  " exception.",
                  FutureWarning,
                  stacklevel=2,
              )
      elif index_none:
          prediction_index = None
  
      # For backwards compatibility, set `predict_*` values
      if prediction_index is not None:
          data.predict_start = prediction_index[0]
          data.predict_end = prediction_index[-1]
          data.predict_dates = prediction_index
      else:
          data.predict_start = None
          data.predict_end = None
          data.predict_dates = None
  
      # Compute out-of-sample observations
      out_of_sample = max(end - (nobs - 1), 0)
      end -= out_of_sample
  
      return start, end, out_of_sample, prediction_index
  
imports: from __future__ import annotations
  from statsmodels.compat.pandas import is_float_index, is_int_index, is_numeric_dtype
  import numbers
  import warnings
  import numpy
  from pandas import DatetimeIndex, Index, Period, PeriodIndex, RangeIndex, Series, Timestamp, date_range, period_range, to_datetime
  from pandas.tseries.frequencies import to_offset
  from statsmodels.base.data import PandasData
  import statsmodels.base.model
  import statsmodels.base.wrapper
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 3 for calling a global function with example usage: get_prediction_index(start, end, nobs, base_index, index, silent, index_none, index_generated, data) and returns: start,  end,  out_of_sample,  prediction_index