class_docstr: Joins the two encoders needed for OneHot transform.
docstr: One hot encode a list of labels.
docstr_tok: ["One","hot","encode","a","list","of","labels","."]
code: def transform(self, labels: List[T]) -> np.ndarray:
      """One hot encode a list of labels."""
      return self.label_binarizer.transform(
          self.int_encoder.transform(labels)
      )
  
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 2 for calling the OneHotEncoder obj's underlying member method code with example usage: obj.transform(labels) and returns: self