def METHOD_NAME(inputs):
    inputstransformed = inputs.detach().clone()
    if flipx:
        if flipy:
            inputstransformed = torch.flip(inputstransformed,[2,3])
        else:
            inputstransformed = torch.flip(inputstransformed,[2])
    else:
        if flipx:
            inputstransformed = torch.flip(inputstransformed,[3])
        else:
            pass
    if swapxy:
        inputstransformed = torch.transpose(inputstransformed,2,3)
    preds, auxpreds = model(inputstransformed.to(gpudevice))
    preds = F.softmax(preds,dim=1)
    assert(len(preds.size()) == 2)
    assert(preds.size()[0] == batchsize)
    assert(preds.size()[1] == 3)
    choices = []
    for b in range(batchsize):
        weights = [preds[b,0],preds[b,1],preds[b,2]]
        choice = rand.choices([0,1,2],weights=weights)[0]
        choices.append(choice)
    return choices