model = ConvTasNet(n_src=2)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
loss = PITLossWrapper(
    lambda x, y: (x - y).pow(2).mean(-1),  # MSE
    pit_from="pw_pt",  # Point in the pairwise matrix.
)
system = System(model, optimizer, loss, train_loader, val_loader)
trainer = Trainer(fast_dev_run=True)
trainer.fit(system)
```
2nd place - carefree-learn
A PyTorch-based automated machine learning (AutoML) solution, carefree-learn provides high-level APIs to make training models using tabular data sets simpler. It features an interface similar to scikit-learn and functions as an end-to-end end pipeline for tabular data sets. It automatically detects feature column types and redundant feature columns, imputes missing values, encodes string columns and categorical columns, and preprocesses numerical columns, among other features. 
3rd Place - TorchExpo