[Feature Request] Target Transforms for keypoints in Image

Torchvision's functional API allows user to explicitly specify angles to rotate the image or points to crop the image. Thus one can apply sane augmentation on target masks or bounding boxes.
But in cases where we have the output as keypoints in the image, for example say human body joint coordinates in given image, if I rotate the image I'll have to find corresponding joint locations in image (via elementary geometry). I felt that this effort off applying corresponding keypoints in augmented image could be handled in pytorch backend and would be very helpful.
Do you think it's worth it to add in torchvision package?
This could come handy in object detection, human joint annotations and maybe even more place

Start with torchvision's functional API and its existing image, mask, and bounding-box transform handling. Clarify the keypoint representation and the transforms to support before designing the API; done would be an agreed implementation scope for updating keypoints alongside augmented images, with coverage for the stated detection and joint-annotation cases.