def fit(self, y):
    'Fit label binarizer\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n            chunked by row.\n            Target values. The 2-d matrix should only contain 0 and 1,\n            represents multilabel classification.\n\n        Returns\n        -------\n        self : returns an instance of self.\n        '
    futures = self.client_.sync(_extract_partitions, y)
    unique = [self.client_.submit(LabelBinarizer._func_unique_classes, f) for (w, f) in futures]
    classes = self.client_.compute(unique, True)
    self.classes_ = rmm_cupy_ary(cp.unique, rmm_cupy_ary(cp.stack, classes, axis=0))
    self.model = LB(**self.kwargs).fit(self.classes_)
    return self
Fit label binarizer

Parameters
----------
y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]
    chunked by row.
    Target values. The 2-d matrix should only contain 0 and 1,
    represents multilabel classification.

Returns
-------
self : returns an instance of self.
def fit(self, y):
    'Fit label binarizer\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n            chunked by row.\n            Target values. The 2-d matrix should only contain 0 and 1,\n            represents multilabel classification.\n\n        Returns\n        -------\n        self : returns an instance of self.\n        '
    futures = self.client_.sync(_extract_partitions, y)
    unique = [self.client_.submit(LabelBinarizer._func_unique_classes, f) for (w, f) in futures]
    classes = self.client_.compute(unique, True)
    self.classes_ = rmm_cupy_ary(cp.unique, rmm_cupy_ary(cp.stack, classes, axis=0))
    self.model = LB(**self.kwargs).fit(self.classes_)
    return self