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 __init__(
      self,
      a_min: BoundType = None,
      a_max: BoundType = None,
      bounce: bool = False,
  ) -> None:
      super().__init__(a_min=a_min, a_max=a_max)
      self._bounce = bounce
      b = ",b" if bounce else ""
      self.name = f"Cl({_f(a_min)},{_f(a_max)}{b})"
      self.checker = utils.BoundChecker(self.a_min, self.a_max)
  
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 1 to create a new Clipping object from inherited base classes: BoundTransform with example: obj = Clipping(a_min, a_max, bounce)