def transform(self, y):
    '\n        Transform and return encoded labels\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing encoded labels\n        '
    parts = self.client_.sync(_extract_partitions, y)
    xform_func = dask.delayed(LabelBinarizer._func_xform)
    meta = rmm_cupy_ary(cp.zeros, 1)
    if self.model.sparse_output:
        meta = cp.sparse.csr_matrix(meta)
    f = [dask.array.from_delayed(xform_func(self.model, part), meta=meta, dtype=cp.float32, shape=(len(y), len(self.classes_))) for (w, part) in parts]
    arr = dask.array.asarray(f)
    return arr.reshape(arr.shape[1:])
Transform and return encoded labels

Parameters
----------
y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]

Returns
-------

arr : Dask.Array backed by CuPy arrays containing encoded labels
def transform(self, y):
    '\n        Transform and return encoded labels\n\n        Parameters\n        ----------\n        y : Dask.Array of shape [n_samples,] or [n_samples, n_classes]\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing encoded labels\n        '
    parts = self.client_.sync(_extract_partitions, y)
    xform_func = dask.delayed(LabelBinarizer._func_xform)
    meta = rmm_cupy_ary(cp.zeros, 1)
    if self.model.sparse_output:
        meta = cp.sparse.csr_matrix(meta)
    f = [dask.array.from_delayed(xform_func(self.model, part), meta=meta, dtype=cp.float32, shape=(len(y), len(self.classes_))) for (w, part) in parts]
    arr = dask.array.asarray(f)
    return arr.reshape(arr.shape[1:])