class_docstr: Joins the two encoders needed for OneHot transform.
docstr: Return number of classes discovered while fitting transform.
docstr_tok: ["Return","number","of","classes","discovered","while","fitting","transform","."]
code: def classes(self) -> np.ndarray:
      """Return number of classes discovered while fitting transform."""
      # pyre-fixme[16]: `LabelBinarizer` has no attribute `classes_`.
      return self.label_binarizer.classes_
  
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 4 for calling the OneHotEncoder obj's underlying member method code with example usage: obj.classes() and returns: self