def fit_transform(self, y):
    '\n        Fit the label encoder and return transformed labels\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n            target values. The 2-d matrix should only contain 0 and 1,\n            represents multilabel classification.\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing encoded labels\n        '
    return self.fit(y).transform(y)
Fit the label encoder and return transformed labels

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

Returns
-------

arr : Dask.Array backed by CuPy arrays containing encoded labels
def fit_transform(self, y):
    '\n        Fit the label encoder and return transformed labels\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n            target values. The 2-d matrix should only contain 0 and 1,\n            represents multilabel classification.\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing encoded labels\n        '
    return self.fit(y).transform(y)