> ### 🚀 The feature

[Haknrven]

          > ### 🚀 The feature

This issue is dedicated for collecting community feedback on the Transforms V2 API. Please review the dedicated blogpost where we describe the API in detail and provide an overview of its features.

We would love to get your thoughts, comments and input in order to improve the API and graduate it from prototype on the near future.

Please also check out #7319 where we collect feedback on some specific design decision, and document as well which APIs may change in the future!

Code example using this image:

import PIL
from torchvision import io, utils
from torchvision.prototype import features, transforms as T
from torchvision.prototype.transforms import functional as F


# Defining and wrapping input to appropriate Tensor Subclasses
path = "COCO_val2014_000000418825.jpg"
img = features.Image(io.read_image(path), color_space=features.ColorSpace.RGB)
# img = PIL.Image.open(path)
bboxes = features.BoundingBox(
    [[2, 0, 206, 253], [396, 92, 479, 241], [328, 253, 417, 332],
     [148, 68, 256, 182], [93, 158, 170, 260], [432, 0, 438, 26],
     [422, 0, 480, 25], [419, 39, 424, 52], [448, 37, 456, 62],
     [435, 43, 437, 50], [461, 36, 469, 63], [461, 75, 469, 94],
     [469, 36, 480, 64], [440, 37, 446, 56], [398, 233, 480, 304],
     [452, 39, 463, 63], [424, 38, 429, 50]],
    format=features.BoundingBoxFormat.XYXY,
    spatial_size=F.get_spatial_size(img),
)
labels = features.Label([59, 58, 50, 64, 76, 74, 74, 74, 74, 74, 74, 74, 74, 74, 50, 74, 74])


# Defining and applying Transforms V2
trans = T.Compose(
    [
        T.ColorJitter(contrast=0.5),
        T.RandomRotation(30),
        T.CenterCrop(480),
    ]
)
img, bboxes, labels = trans(img, bboxes, labels)


# Visualizing results
viz = utils.draw_bounding_boxes(F.to_image_tensor(img), boxes=bboxes)
F.to_pil_image(viz).show()

Originally posted by @Haknrven in #6753 (comment)