def inverse_transform(self, y, threshold=None):
    '\n        Invert a set of encoded labels back to original labels\n\n        Parameters\n        ----------\n\n        y : Dask.Array of shape [n_samples, n_classes] containing encoded\n            labels\n\n        threshold : float This value is currently ignored\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing original labels\n        '
    parts = self.client_.sync(_extract_partitions, y)
    inv_func = dask.delayed(LabelBinarizer._func_inv_xform)
    dtype = self.classes_.dtype
    meta = rmm_cupy_ary(cp.zeros, 1, dtype=dtype)
    f = [dask.array.from_delayed(inv_func(self.model, part, threshold), dtype=dtype, shape=(y.shape[0],), meta=meta) for (w, part) in parts]
    ret = dask.array.stack(f, axis=0)
    return ret.reshape(ret.shape[1:])
Invert a set of encoded labels back to original labels

Parameters
----------

y : Dask.Array of shape [n_samples, n_classes] containing encoded
    labels

threshold : float This value is currently ignored

Returns
-------

arr : Dask.Array backed by CuPy arrays containing original labels
def inverse_transform(self, y, threshold=None):
    '\n        Invert a set of encoded labels back to original labels\n\n        Parameters\n        ----------\n\n        y : Dask.Array of shape [n_samples, n_classes] containing encoded\n            labels\n\n        threshold : float This value is currently ignored\n\n        Returns\n        -------\n\n        arr : Dask.Array backed by CuPy arrays containing original labels\n        '
    parts = self.client_.sync(_extract_partitions, y)
    inv_func = dask.delayed(LabelBinarizer._func_inv_xform)
    dtype = self.classes_.dtype
    meta = rmm_cupy_ary(cp.zeros, 1, dtype=dtype)
    f = [dask.array.from_delayed(inv_func(self.model, part, threshold), dtype=dtype, shape=(y.shape[0],), meta=meta) for (w, part) in parts]
    ret = dask.array.stack(f, axis=0)
    return ret.reshape(ret.shape[1:])