MeanAveragePrecision for custom area definitions

Nobody has claimed this yet.

## 🚀 Feature

MeanAveragePrecision metric computes mAP and mAR for the following area definition:
```
self.bbox_area_ranges = {
"all": (float(0**2), float(1e5**2)),
"small": (float(0**2), float(32**2)),
"medium": (float(32**2), float(96**2)),
"large": (float(96**2), float(1e5**2)),
}
```
I would like to provide a different definition, like:
```
MeanAveragePrecision(areas={
"all": (float(0**2), float(1e5**2)),
"xsmall": (float(0**2), float(16**2)),
"small": (float(16**2), float(32**2)),
"medium": (float(32**2), float(96**2)),
"large": (float(96**2), float(1e5**2)),
})
```

### Motivation

The default does not necessarily make sense for datasets other than COCO.

### Pitch

Instead of returning the metrics of the default areas, return the summarized metrics for the provided area definition.

### Alternatives

-

### Additional context

-

Start at the MeanAveragePrecision entry point and trace how the default area definitions are stored and summarized. Define the expected behavior for a caller-provided areas mapping, then update the relevant metric tests so custom categories such as xsmall are included in the returned summaries.

Written by the indexing model from the issue text.