def METHOD_NAME(self):
    # Construct different sets of parameters
    torch_params, apex_params = make_params(7, make_copy=True)
    with torch.no_grad():
        torch_params[0].grad.view(-1)[0] = 1.23
        apex_params[0].grad.view(-1)[0] = 3.21
    # Apply gradient clipping
    torch_norm = torch.nn.utils.clip_grad_norm_(
        torch_params,
        0.54321,
    )
    apex_norm = clip_grad_norm_(
        apex_params,
        0.54321,
    )
    # Make sure PyTorch and Apex get different results
    self.assertRaises(
        AssertionError,
        torch.testing.assert_close,
        apex_norm, torch_norm,
        rtol=1e-3,
        atol=1e-20,
        check_dtype=False,
    )
    for torch_p, apex_p in zip(torch_params, apex_params):
        self.assertRaises(
            AssertionError,
            torch.testing.assert_close,
            apex_p.grad, torch_p.grad,
            rtol=1e-3,
            atol=1e-20,
        )