code: def convert_pred_quantiles_to_interval(y_pred, inplace=False):
      """Convert quantile predictions to interval predictions.
  
      Parameters
      ----------
      y_pred : pd.DataFrame
          Column has multi-index: first level is variable name from y in fit,
              second level being the values of alpha passed to the function.
          Row index is fh. Entries are quantile forecasts, for var in col index,
              at quantile probability in second col index, for the row index.
      inplace : bool, optional, default=False
          whether to copy the input data frame (False), or modify (True)
  
      Returns
      -------
      y_pred : pd.DataFrame
          Column has multi-index: first level is variable name from y in fit,
              second level coverage fractions for which intervals were computed.
                  in the same order as in input `coverage`.
              Third level is string "lower" or "upper", for lower/upper interval end.
          Row index is fh. Entries are forecasts of lower/upper interval end,
              for var in col index, at nominal coverage in selencond col index,
              lower/upper depending on third col index, for the row index.
              Upper/lower interval end forecasts are equivalent to
              quantile forecasts at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.
      """
      if not inplace:
          y_pred = y_pred.copy()
  
      # all we need to do is to replace the index with var_names/alphas
      # var_names will be the same as interval level 0
      idx = y_pred.columns
      var_names = idx.get_level_values(0)
  
      # coverages we compute by the coverage/alphas formula correspondence
      alphas = idx.get_level_values(1)
      alphas = np.array(alphas.copy())
      coverages = 2 * np.abs(0.5 - alphas)
      lower_upper = ["lower" if a <= 0.5 else "upper" for a in alphas]
  
      # idx returned by _predict_quantiles
      #   is 3-level MultiIndex with variable names, coverages, lower/upper
      int_idx = pd.MultiIndex.from_arrays(
          [var_names, coverages, lower_upper]
      )
      y_pred.columns = int_idx
  
      return y_pred
  
imports: import numpy
  import pandas
  from sktime.datatypes._proba._registry import MTYPE_LIST_PROBA
label_desc: Use image node_id 4 for calling a global function with example usage: convert_pred_quantiles_to_interval(y_pred, inplace) and returns: y_pred