def METHOD_NAME(self, weights, abort_signal):
    self.model.load_state_dict(weights)
    self.model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for i, (images, labels) in enumerate(self.test_loader):
            if abort_signal.triggered:
                return 0
            images, labels = images.to(self.device), labels.to(self.device)
            output = self.model(images)
            _, pred_label = torch.max(output, 1)
            correct += (pred_label == labels).sum().item()
            total += images.size()[0]
        metric = correct / float(total)
    return metric