Fix for 0d numpy arrays

    dangerous (!) for large datasets which don't fit into memory.

    """

    Xs, ys, ws, ids = [], [], [], []

    print("to_numpy()")

    print("len(self)")

    print(len(self))

    for (X_b, y_b, w_b, ids_b) in self.itershards():

      Xs.append(X_b)

      ys.append(y_b)

      ws.append(w_b)

      ids.append(np.squeeze(ids_b))

    #print("to_numpy()")

    #for y_b, ids_b in zip(ys, ids):

    #  print("y_b.shape, ids_b.shape")

    #  print(y_b.shape, ids_b.shape)

    print("len(ids)")

    print(len(ids))

    print("[type(ids_b) for ids_b in ids]") 

    print([type(ids_b) for ids_b in ids]) 

    #print("[len(ids_b) for ids_b in ids]")

    #print([len(ids_b) for ids_b in ids])

    print("sum([len(ys_b) for ys_b in ys])")

    print(sum([len(ys_b) for ys_b in ys]))

    print("sum([len(ws_b) for ws_b in ws])")

    print(sum([len(ws_b) for ws_b in ws]))

      ids.append(np.atleast_1d(np.squeeze(ids_b)))

    np.concatenate(ids)

    return (np.vstack(Xs), np.vstack(ys), np.vstack(ws),

            np.concatenate(ids))