* `VisualizerCallback` is refactored into a base `VisualizerCallbackBase`
class which is used to construct `VisualizerCallbackImage` via inheritance.
* Fix bug in `VisualizerCallback.on_test_end`, where `pl_module.logger`
was accessed instead of iterating over `trainer.loggers`
(wandb errort silently before iirc)
* Skeleton for `VisualizerCallbackMetric` is added

PyTorch provides a function for logging Figure objects, so lets not
reinvent the wheel.

https://pytorch.org/docs/stable/tensorboard.html?highlight=add_figure#torch.utils.tensorboard.writer.SummaryWriter.add_figure

Otherwise, matplotlib starts to complain and objects aren't gced
properly

`Visualizer` is needed by all Callbacks for writing images to disk

Every metric that should be visualized needs to implement its own
`generate_figure` function, and the resulting plot is then saved by
`VisualizerCallbackMetric`

…alib-1 into feature/metrics_visualizer

* Needed to change signature of `AnomalyModule._collect_outputs` for
pixel-wise metrics as we need spatial dimensions for
connected-componenta-analysis in AUPRO
* Add AUPRO, which uses kornia and the fact that
per-region overlap == per-region tpr for fast AUPRO computation
* Updated docstrings for AUPR/AUROC

Due to bugs in CLI, feature could not be tested well

Since unique fpr/tpr curves are generated for each label,
we cannot use fpr_index across calls ro `roc`. Instead, we now
bilinearly resample generated pro curves at `fpr <= self.fpr_limit`
to fixed sampling points, and then aggregate over the resampled curve.

We follow a scheme similar to the ROC curve, where the PRO curve is
composed of per-region TPR plotted against global FPR.

We now prepend the last part, i.e. `VisualizerCallbackBase` -> `BaseVisualizerCallback`