Extend predictors to zero-cost case

In the zerocost branch, `Ensemble` has been extended to the Zero Cost case and contains a single option for its base predictor, `XGBoost`. The `XGBoost` predictor has been adapted to the zero-cost case by implementing a `set_pre_computations` function, as well as modifying the `BaseTree` class. Currently, the `Ensemble` class supports only this single predictor:

```python
trainable_predictors = {
"xgb": XGBoost(
ss_type=self.ss_type, zc=self.zc, encoding_type="adjacency_one_hot", zc_only=self.zc_only
)
}
```
Should all other predictors be available in the merged `Ensemble` class and extended to the zero-cost case?