class_docstr: Hacky way to sample in the space defined by the parametrization.
  Given an vector of values between 0 and 1,
  the transform method samples in the bounds if provided,
  or using the provided function otherwise.
  This is used for samplers.
  Code of parametrization and/or this helper should definitely be
  updated to make it simpler and more robust
class_docstr_tok: ["Hacky","way","to","sample","in","the","space","defined","by","the","parametrization",".","Given","an","vector","of","values","between","0","and","1",",","the","transform","method","samples","in","the","bounds","if","provided",",","or","using","the","provided","function","otherwise",".","This","is","used","for","samplers",".","Code","of","parametrization","and\/or","this","helper","should","definitely","be","updated","to","make","it","simpler","and","more","robust"]
code: def _warn(self) -> None:
      warnings.warn(
          f"Failed to find bounds for {self.reference}, quasi-random optimizer may be inefficient.\n"
          "Please open an issue on Nevergrad github"
      )
  
imports: import warnings
  import contextlib
  import itertools
  import numpy
  import nevergrad.common.typing
  from .None import core
  from .None import container
  from .None import _layering
  from .None import _datalayers
  from .None import data
  from .None import choice
  from .None import transforms
label_desc: Use image node_id 2 for calling the Normalizer obj's underlying member method code with example usage: obj._warn() without return types