class_docstr: Convert categorical parameters (unordered ChoiceParameters) to
  one-hot-encoded parameters.
  
  Does not convert task parameters.
  
  Parameters will be one-hot-encoded, yielding a set of RangeParameters,
  of type float, on [0, 1]. If there are two values, one single RangeParameter
  will be yielded, otherwise there will be a new RangeParameter for each
  ChoiceParameter value.
  
  In the reverse transform, floats can be converted to a one-hot encoded vector
  using one of two methods:
  
  Strict rounding: Choose the maximum value. With levels ['a', 'b', 'c'] and
      float values [0.2, 0.4, 0.3], the restored parameter would be set to 'b'.
      Ties are broken randomly, so values [0.2, 0.4, 0.4] is randomly set to 'b'
      or 'c'.
  
  Randomized rounding: Sample from the distribution. Float values
      [0.2, 0.4, 0.3] are transformed to 'a' w.p.
      0.2/0.9, 'b' w.p. 0.4/0.9, or 'c' w.p. 0.3/0.9.
  
  Type of rounding can be set using transform_config['rounding'] to either
  'strict' or 'randomized'. Defaults to strict.
  
  Transform is done in-place.
code: def _transform_search_space(
      self, search_space: SearchSpace
  ) -> SearchSpace:
      transformed_parameters: Dict[str, Parameter] = {}
      for p_name, p in search_space.parameters.items():
          if p_name in self.encoded_parameters:
              if p.is_fidelity:
                  raise ValueError(
                      f"Cannot one-hot-encode fidelity parameter {p_name}"
                  )
              for new_p_name in self.encoded_parameters[p_name]:
                  transformed_parameters[new_p_name] = RangeParameter(
                      name=new_p_name,
                      parameter_type=ParameterType.FLOAT,
                      lower=0,
                      upper=1,
                  )
          else:
              transformed_parameters[p_name] = p
      return construct_new_search_space(
          search_space=search_space,
          parameters=list(transformed_parameters.values()),
          parameter_constraints=[
              pc.clone_with_transformed_parameters(
                  transformed_parameters=transformed_parameters
              )
              for pc in search_space.parameter_constraints
          ],
      )
  
imports: from typing import Dict, List, Optional, TYPE_CHECKING, TypeVar
  import numpy
  from ax.core.observation import Observation, ObservationFeatures
  from ax.core.parameter import ChoiceParameter, Parameter, ParameterType, RangeParameter
  from ax.core.search_space import SearchSpace
  from ax.core.types import TParameterization
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.rounding import randomized_onehot_round, strict_onehot_round
  from ax.modelbridge.transforms.utils import construct_new_search_space
  from ax.models.types import TConfig
  from sklearn.preprocessing import LabelBinarizer, LabelEncoder
label_desc: Use image node_id 3 for calling the OneHot obj's underlying member method code with example usage: obj._transform_search_space(search_space) and returns: construct_new_search_space