class_docstr: Base implementation for encoding categorical values, uses
  :py:class:`~cuml.preprocessing.LabelEncoder` for obtaining unique values.
  
  Parameters
  ----------
  
  handle : cuml.Handle
      Specifies the cuml.handle that holds internal CUDA state for
      computations in this model. Most importantly, this specifies the CUDA
      stream that will be used for the model's computations, so users can
      run different models concurrently in different streams by creating
      handles in several streams.
      If it is None, a new one is created.
  verbose : int or boolean, default=False
      Sets logging level. It must be one of `cuml.common.logger.level_*`.
      See :ref:`verbosity-levels` for more info.
  output_type : {'input', 'array', 'dataframe', 'series', 'df_obj',         'numba', 'cupy', 'numpy', 'cudf', 'pandas'}, default=None
      Return results and set estimator attributes to the indicated output
      type. If None, the output type set at the module level
      (`cuml.global_settings.output_type`) will be used. See
      :ref:`output-data-type-configuration` for more info.
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docstr: Returns categories used for the one hot encoding in the correct order.
code: def categories_(self):
      """Returns categories used for the one hot encoding in the correct order."""
      return [self._encoders[f].classes_ for f in self._features]
  
imports: import warnings
  from typing import List, Optional, TypeVar
  import cuml.internals.logger
  from cudf import DataFrame, Series
  from cuml import Base
  from cuml.common.doc_utils import generate_docstring
  from cuml.common.exceptions import NotFittedError
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import, gpu_only_import_from
  from cuml.preprocessing import LabelEncoder
label_desc: Use image node_id 6 for calling the BaseEncoder obj's underlying member method code with example usage: obj.categories_() and returns: unknown