class_docstr: Joins the two encoders needed for OneHot transform.
code: def __init__(self, values: List[T]) -> None:
      self.int_encoder = LabelEncoder().fit(values)
      self.label_binarizer = LabelBinarizer().fit(
          self.int_encoder.transform(values)
      )
  
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 1 to create a new OneHotEncoder object  with example: obj = OneHotEncoder(values)