def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
def _add_implementor(rpc_code, blocking, fn):

    # Validate the argument types.
    if type(rpc_code) is not int:
        raise TypeError("Expected int, got %r instead" % type(rpc_code))
    if type(blocking) is not bool:
        raise TypeError("Expected bool, got %r instead" % type(blocking))
    if not callable(fn):
        raise TypeError("Expected callable, got %r instead" % type(fn))

    # Validate the RPC code.
    if rpc_code in rpcMap:
        try:
            msg = "Duplicated RPC implementors for code %d: %s and %s"
            msg %= (rpc_code, rpcMap[rpc_code][0].__name__, fn.__name__)
        except Exception:
            msg = "Duplicated RPC implementors for code: %d" % rpc_code
        raise SyntaxError(msg)

    # TODO: use introspection to validate the function signature

    # Register the implementor.
    rpcMap[rpc_code] = (fn, blocking)

    # Return the implementor. No wrapping is needed! :)
    return fn