class_docstr: Wrapper around scikit-learn multiclass classifiers that allows to
  choose different multiclass strategies.
  
  The input can be any kind of cuML compatible array, and the output type
  follows cuML's output type configuration rules.
  
  Berofe passing the data to scikit-learn, it is converted to host (numpy)
  array. Under the hood the data is partitioned for binary classification,
  and it is transformed back to the device by the cuML estimator. These
  copies back and forth the device and the host have some overhead. For more
  details see issue https://github.com/rapidsai/cuml/issues/2876.
  
  Examples
  --------
  
  .. code-block:: python
  
      >>> from cuml.linear_model import LogisticRegression
      >>> from cuml.multiclass import MulticlassClassifier
      >>> from cuml.datasets.classification import make_classification
  
      >>> X, y = make_classification(n_samples=10, n_features=6,
      ...                            n_informative=4, n_classes=3,
      ...                            random_state=137)
  
      >>> cls = MulticlassClassifier(LogisticRegression(), strategy='ovo')
      >>> cls.fit(X,y)
      MulticlassClassifier()
      >>> cls.predict(X)
      array([2, 0, 2, 2, 2, 1, 1, 0, 1, 1])
  
  Parameters
  ----------
  estimator : cuML estimator
  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.
  strategy: string {'ovr', 'ovo'}, default='ovr'
      Multiclass classification strategy: 'ovr': one vs. rest or 'ovo': one
      vs. one
  
  Attributes
  ----------
  classes_ : float, shape (`n_classes_`)
      Array of class labels.
  n_classes_ : int
      Number of classes.
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code: def get_param_names(self):
      return super().get_param_names() + ["estimator", "strategy"]
  
imports: import cuml.internals
  from cuml.internals.array import CumlArray
  from cuml.internals.base import Base
  from cuml.internals.import_utils import has_sklearn
  from cuml.internals.mixins import ClassifierMixin
  from cuml.common.doc_utils import generate_docstring
  from cuml.common import input_to_host_array, input_to_host_array_with_sparse_support
  from cuml.internals.input_utils import input_to_cupy_array, determine_array_type_full
  from cuml.internals.array_sparse import SparseCumlArray
  from cuml.internals import _deprecate_pos_args
label_desc: Use image node_id 7 for calling the MulticlassClassifier obj's underlying member method code with example usage: obj.get_param_names() and returns: unknown