def METHOD_NAME(self, dtype=torch.float):
    if not torch.cuda.is_available():
        return
    from mmcv.ops import SoftmaxFocalLoss
    alpha = 0.25
    gamma = 2.0
    for case in inputs:
        np_x = np.array(case[0])
        np_y = np.array(case[1])
        x = torch.from_numpy(np_x).cuda().type(dtype)
        x.requires_grad_()
        y = torch.from_numpy(np_y).cuda().long()
        floss = SoftmaxFocalLoss(gamma, alpha)
        if _USING_PARROTS:
            # gradcheck(floss, (x, y),
            #           no_grads=[y])
            pass
        else:
            gradcheck(floss, (x, y), eps=1e-2, atol=1e-2)