
The Transform Classes make sure that they apply the same random transforms to all the inputs to ensure consistent results.

The functional API has been updated to support all necessary signal processing kernels (resizing, cropping, affine transforms, padding etc) for all inputs:

```python
from torchvision.transforms.v2 import functional as F


# High-level dispatcher, accepts any supported input type, fully BC
F.resize(inpt, size=[224, 224])
# Image tensor kernel
F.resize_image_tensor(img_tensor, size=[224, 224], antialias=True) 
# PIL image kernel
F.resize_image_pil(img_pil, size=[224, 224], interpolation=BILINEAR)
# Video kernel
F.resize_video(video, size=[224, 224], antialias=True) 
# Mask kernel
F.resize_mask(mask, size=[224, 224])
# Bounding box kernel
F.resize_bounding_box(bbox, size=[224, 224], spatial_size=[256, 256])
