How to integrat with regular pytorch models

Hi!

I am currently looking into rephrasing a classification problem into a learning-to-rank problem. I have found your repo and it looks quite promising, you have definitely put a lot of effort into this.

My main question is about how I can use parts of this repo, import them and use them in my own model which operates on 1D vectors of inputs and outputs a scalar prediction per input example. In total It produces a 1D Tensor of roughly 10k predictions between 0 and 1 and I have a 1D tensor of labels between 0 and 1 of identical length to compare them with.

For the losses, taking lambdaLoss as example:
```
:param y_pred: predictions from the model, shape [batch_size, slate_length]
:param y_true: ground truth labels, shape [batch_size, slate_length]
```
How can I apply that to my output/labels? Do I have to sample n negatives for each positive to obtain m slates of length n+1 before I can feed it into the loss functions? In that case the y_pred and y_true would have the shape ```[m, n+1]```. Is there a way or (preferably list-wise) loss function where I can directly drop my 1D output/labels vectors into (just like pytorchs BCE or MSE losses)?

For the models:

How can I make the architecture of my model more LTR-sensitive? Assume I have a network of fully connected layers which passes its output through a sigmoid layer to predict class probabilites. What would have to change to optimize the architecture for a Learning-To-Rank task? Which of the Ideas and implementations of this repo can I leverage to accomplish this?

Thanks in advance for your time and effort. I would be more than happy to elaborate if some of my questions are confusion or dont make sense.

Cheers,
Florin