code: def _preprocess(self, inputs: ep.TensorType) -> ep.TensorType:
      if self.bounds == self._model.bounds:
          return inputs
  
      # from bounds to (0, 1)
      min_, max_ = self.bounds
      x = (inputs - min_) / (max_ - min_)
  
      # from (0, 1) to wrapped model bounds
      min_, max_ = self._model.bounds
      return x * (max_ - min_) + min_
  
imports: from typing import TypeVar, Callable, Optional, Tuple, Any
  from abc import ABC, abstractmethod
  import copy
  import eagerpy
  from ..types import Bounds, BoundsInput, Preprocessing
  from ..devutils import atleast_kd
label_desc: Use image node_id 5 for calling the TransformBoundsWrapper obj's underlying member method code with example usage: obj._preprocess(inputs) and returns: unknown, inputs