class_docstr: Bounds all real values into [a_min, a_max] using clipping (not bijective).
  
  Parameters
  ----------
  a_min: float or None
      lower bound
  a_max: float or None
      upper bound
  bounce: bool
      bounce (once) on borders instead of just clipping
class_docstr_tok: ["Bounds","all","real","values","into","[","a_min",",","a_max","]","using","clipping","(","not","bijective",")",".","Parameters","--","--","--","--","--","a_min",":","float","or","None","lower","bound","a_max",":","float","or","None","upper","bound","bounce",":","bool","bounce","(","once",")","on","borders","instead","of","just","clipping"]
code: def backward(self, y: np.ndarray) -> np.ndarray:
      self._check_shape(y)
      if not self.checker(y):
          raise ValueError(
              f"Only data between {self.a_min} and {self.a_max} can be transformed back.\n"
              f"Got: {y}"
          )
      return y
  
imports: import uuid
  import itertools
  import numpy
  from scipy import stats
  import nevergrad.common.typing
  from .None import utils
label_desc: Use image node_id 3 for calling the Clipping obj's underlying member method code with example usage: obj.backward(y) and returns: y