def METHOD_NAME(dataset, instance_data):
    tracks = {}
    label_cat = dataset.categories()[dm.AnnotationType.label]
    for item in dataset:
        # NOTE: MOT frames start from 1
        # job has an offset, for task offset is 0
        frame_number = int(item.id) - 1 + instance_data.start
        frame_number = instance_data.abs_frame_id(frame_number)
        for ann in item.annotations:
            if ann.type != dm.AnnotationType.bbox:
                continue
            occluded = ann.attributes.pop('occluded', False) is True
            track_id = ann.attributes.pop('track_id', None)
            attributes = [
                instance_data.Attribute(name=n, value=str(v))
                for n, v in ann.attributes.items()
            ]
            if track_id is None:
                # Extension. Import regular boxes:
                instance_data.add_shape(instance_data.LabeledShape(
                    type='rectangle',
                    label=label_cat.items[ann.label].name,
                    points=ann.points,
                    occluded=occluded,
                    z_order=ann.z_order,
                    group=0,
                    frame=frame_number,
                    attributes=attributes,
                    source='manual',
                ))
                continue
            shape = instance_data.TrackedShape(
                type='rectangle',
                points=ann.points,
                occluded=occluded,
                outside=False,
                keyframe=True,
                z_order=ann.z_order,
                frame=frame_number,
                attributes=attributes,
                source='manual',
            )
            # build trajectories as lists of shapes in track dict
            if track_id not in tracks:
                tracks[track_id] = instance_data.Track(
                    label_cat.items[ann.label].name, 0, 'manual', [])
            tracks[track_id].shapes.append(shape)
    for track in tracks.values():
        # MOT annotations do not require frames to be ordered
        track.shapes.sort(key=lambda t: t.frame)
        # insert outside=True in skips between the frames track is visible
        prev_shape_idx = 0
        prev_shape = track.shapes[0]
        for shape in track.shapes[1:]:
            has_skip = instance_data.frame_step < shape.frame - prev_shape.frame
            if has_skip and not prev_shape.outside:
                prev_shape = prev_shape._replace(outside=True,
                        frame=prev_shape.frame + instance_data.frame_step)
                prev_shape_idx += 1
                track.shapes.insert(prev_shape_idx, prev_shape)
            prev_shape = shape
            prev_shape_idx += 1
        # Append a shape with outside=True to finish the track
        last_shape = track.shapes[-1]
        if last_shape.frame + instance_data.frame_step <= \
                int(instance_data.meta[instance_data.META_FIELD]['stop_frame']):
            track.shapes.append(last_shape._replace(outside=True,
                frame=last_shape.frame + instance_data.frame_step)
            )
        instance_data.add_track(track)