code: def nn_y(self, y, dims=None, k = 1, radius=np.inf, eps=0.0, p=2):
          
          if dims is None:
              assert len(y) == self.dim_y
              k_y = min(k, self.__len__())
              return self._nn(DATA_Y, y, k=k_y, radius=radius, eps=eps, p=p)
          else:
              return self.nn_y_sub(y, dims, k, radius, eps, p)
docs: Find the k nearest neighbors of y in the observed output data
          @see Databag.nn() for argument description
          @return  distance and indexes of found nearest neighbors.