def METHOD_NAME(self, raw_client_grad_list):
    # print(f"raw_client_grad_list = {raw_client_grad_list}")
    # Foolsgold uses the last layer's gradient/weights as the importance feature.
    ret_feature_vector_list = []
    for idx in range(len(raw_client_grad_list)):
        raw_grad = raw_client_grad_list[idx]
        (p, grads) = raw_grad
        # Get last key-value tuple
        (weight_name, importance_feature) = list(grads.items())[-2]
        # print(importance_feature)
        feature_len = np.array(
            importance_feature.cpu().data.detach().numpy().shape
        ).prod()
        feature_vector = np.reshape(
            importance_feature.cpu().data.detach().numpy(), feature_len
        )
        ret_feature_vector_list.append(feature_vector)
    return ret_feature_vector_list