[ENH] Allow `TorchExperiment` metric to set "higher is better"

[SimonBlanke]

The TorchExperiment assumes all metrics should be minimized (lower is better) by default. This can be seen here in this line. However, many metrics should be maximized (accuracy, F1, AUC), requiring users to manually negate them or wrap them in custom logic. The integration doesn't auto-detect metric direction from common naming patterns.

Sklearn's OptCV uses _coerce_to_scorer_and_sign (from _skl_metrics.py) to automatically detect metric direction. Maybe we can write something similar for the TorchExperiment.

[amitsubhashchejara]

Okay I will check this.