def METHOD_NAME(self, pt_path):
    assert osp.exists(osp.join(pt_path, "batch_model.bgi"))
    checkpoint = torch.load(osp.join(pt_path, "batch_model.bgi"), map_location=lambda storage, loc: storage)
    state_dict = self.model.state_dict()
    trained_dict = {k: v for k, v in checkpoint.items() if k in state_dict}
    state_dict.update(trained_dict)
    self.model.load_state_dict(state_dict)
    self.model.eval()
    batch_acc = []
    for idx, data in enumerate(self.test_loader):
        x, y = data
        x = x.to(self.device)
        y = y.long().to(self.device)
        with torch.no_grad():
            y_hat = self.model(x)
        predict = F.softmax(y_hat, dim=1).argmax(1)
        correct = predict.eq(y).sum().item()
        total = y.size(0)
        acc = correct / total
        batch_acc.append(acc)
    acc_mean = np.mean(batch_acc)
    return acc_mean