def radius_neighbors(self, X, radius=None, return_distance=True):
        if not hasattr(self, 'hash_functions_'):
            raise ValueError("estimator should be fitted.")

        if radius is None:
            radius = self.radius

        X = check_array(X, accept_sparse='csr')

        neighbors, distances = [], []
        bin_queries, max_depth = self._query(X)
        for i in range(X.shape[0]):

            neighs, dists = self._get_radius_neighbors(X[[i]], max_depth[i],
                                                       bin_queries[i], radius)
            neighbors.append(neighs)
            distances.append(dists)

        if return_distance:
            return _array_of_arrays(distances), _array_of_arrays(neighbors)
        else:
            return _array_of_arrays(neighbors)
Finds the neighbors within a given radius of a point or points.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neighbors/approximate.py/LSHForest.radius_neighbors