class_docstr: Binary indicators for missing values.
  
  Note that this component typically should not be used in a vanilla
  :class:`Pipeline` consisting of transformers and a classifier, but rather
  could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`.
  
  Parameters
  ----------
  missing_values : number, string, np.nan (default) or None
      The placeholder for the missing values. All occurrences of
      `missing_values` will be imputed. For pandas' dataframes with
      nullable integer dtypes with missing values, `missing_values`
      should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.
  
  features : str, default=None
      Whether the imputer mask should represent all or a subset of
      features.
  
      - If "missing-only" (default), the imputer mask will only represent
        features containing missing values during fit time.
      - If "all", the imputer mask will represent all features.
  
  sparse : boolean or "auto", default=None
      Whether the imputer mask format should be sparse or dense.
  
      - If "auto" (default), the imputer mask will be of same type as
        input.
      - If True, the imputer mask will be a sparse matrix.
      - If False, the imputer mask will be a numpy array.
  
  error_on_new : boolean, default=None
      If True (default), transform will raise an error when there are
      features with missing values in transform that have no missing values
      in fit. This is applicable only when ``features="missing-only"``.
  
  Attributes
  ----------
  features_ : ndarray, shape (n_missing_features,) or (n_features,)
      The features indices which will be returned when calling ``transform``.
      They are computed during ``fit``. For ``features='all'``, it is
      to ``range(n_features)``.
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn.impute import MissingIndicator
  >>> X1 = np.array([[np.nan, 1, 3],
  ...                [4, 0, np.nan],
  ...                [8, 1, 0]])
  >>> X2 = np.array([[5, 1, np.nan],
  ...                [np.nan, 2, 3],
  ...                [2, 4, 0]])
  >>> indicator = MissingIndicator()
  >>> indicator.fit(X1)
  MissingIndicator()
  >>> X2_tr = indicator.transform(X2)
  >>> X2_tr
  array([[False,  True],
         [ True, False],
         [False, False]])
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code: def get_param_names(self):
      return super().get_param_names() + [
          "missing_values",
          "features",
          "sparse",
          "error_on_new",
      ]
  
imports: from ....internals import _deprecate_pos_args
  from ....common.array_descriptor import CumlArrayDescriptor
  from ....internals.array_sparse import SparseCumlArray
  from ..utils.validation import FLOAT_DTYPES
  from ..utils.validation import check_is_fitted
  from cuml.internals.mixins import AllowNaNTagMixin, SparseInputTagMixin, StringInputTagMixin
  from ..utils.skl_dependencies import BaseEstimator, TransformerMixin
  from ....thirdparty_adapters import _get_mask, _masked_column_median, _masked_column_mean, _masked_column_mode
  from cuml.internals.safe_imports import gpu_only_import_from
  import cuml
  from cuml.internals.safe_imports import gpu_only_import
  import numbers
  import warnings
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 2 for calling the MissingIndicator obj's underlying member method code with example usage: obj.get_param_names() and returns: unknown