def setup_keyring(keyring_name):
    '''Setup the keyring'''
    keyring_path = os.path.join("test", "outputdata", keyring_name)
    # Delete the entire keyring
    shutil.rmtree(keyring_path, ignore_errors=True)
    os.makedirs(keyring_path)
    gpg = GPG(gnupghome=keyring_path, gpgbinary="gpg")
    for key_name in ["key1_private", "key1_public"]:
        with open(os.path.join("test", "inputdata", key_name + ".txt"), "r") as keyfile:
            key_str = "".join(keyfile.readlines())
        import_result = gpg.import_keys(key_str)
        print("Import result:", type(import_result))
        print(import_result.__dict__)
        if import_result.count == 1 and len(set(import_result.fingerprints)) == 1:
            print("Got one import result")
    return gpg
def gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads