docstr: Transform the response values by linearly scaling
  them between zero and one and then use inverse scaling.
code: def transform_response_values(self, values: np.ndarray) -> np.ndarray:
      """Transform the response values by linearly scaling
      them between zero and one and then use inverse scaling.
      """
      min_y = self._min_y - (
          self._percentile - self._min_y
      )  # Subtract the difference between the percentile and the minimum
      min_y -= (
          constants.VERY_SMALL_NUMBER
      )  # Minimal value to avoid numerical issues in the log scaling below
      # linear scaling
      # prevent diving by zero
  
      min_y[np.where(min_y == self._max_y)] *= 1 - 10**-10
  
      values = (values - min_y) / (self._max_y - min_y)
      values = 1 - 1 / values
      return values
  
file_path: SMAC3/smac/runhistory/encoder/inverse_scaled_encoder.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from smac import constants
  from smac.runhistory.encoder.encoder import RunHistoryEncoder
  from smac.utils.logging import get_logger
label_desc: Use image node_id 2 for calling the RunHistoryInverseScaledEncoder obj's underlying member method code with example usage: obj.transform_response_values(values) and returns: values