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 __init__(
      self,
      reference: core.Parameter,
      unbounded_transform: tp.Optional[trans.Transform] = None,
      only_sampling: bool = False,
  ) -> None:
      self.reference = reference.spawn_child()
      self.reference.freeze()
      # initial check
      parameter = self.reference.spawn_child()
      parameter.set_standardized_data(
          np.linspace(-1, 1, self.reference.dimension)
      )
      expected = parameter.get_standardized_data(
          reference=self.reference
      )
      self._ref_arrays = list_data(self.reference)
      arrays = list_data(parameter)
      check = np.concatenate(
          [
              x.get_standardized_data(reference=y)
              for x, y in zip(arrays, self._ref_arrays)
          ],
          axis=0,
      )
      self.working = True
      if not np.allclose(check, expected):
          self.working = False
          self._warn()
      self._only_sampling = only_sampling
      self.unbounded_transform = (
          trans.ArctanBound(0, 1)
          if unbounded_transform is None
          else unbounded_transform
      )
      self.fully_bounded = all(
          bool(_fully_bounded_layers(data)) for data in self._ref_arrays
      )
  
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 1 to create a new Normalizer object  with example: obj = Normalizer(reference, unbounded_transform, only_sampling)