def METHOD_NAME(input_weights):
    net.load_state_dict(input_weights)
    # (optional) use GPU to speed things up
    net.to(DEVICE)
    correct = 0
    total = 0
    # since we're not training, we don't need to calculate the gradients for our outputs
    with torch.no_grad():
        for data in testloader:
            # (optional) use GPU to speed things up
            images, labels = data[0].to(DEVICE), data[1].to(DEVICE)
            # calculate outputs by running images through the network
            outputs = net(images)
            # the class with the highest energy is what we choose as prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f"Accuracy of the network on the 10000 test images: {100 * correct // total} %")
    # return evaluation metrics
    return 100 * correct // total