Current limitation on transforms

The torchvision transforms now have 2 backends (PIL and Tensor), here are some functional mismatch between them and some may-be-useful features that neither of them support. Details are listed in transforms.py and functional.py.

Supported by PIL but not Tensor:

1. Fill value for pad and random crop.
2. Tensor images do not support many modes due to lack of metadata (maybe not possible to address). For instance, the adjust_* functions and autoaugment related functions.
3. Tensors only support 3 interpolation modes (bilinear, linear, nearest).
4. Tensors only support transformations on RGB images

Crop with crop size larger than the original image. #3297 Solved by #3333

Supported by Tensor but not PIL:

1. Normalize (probably of no use for PIL images).
2. Erase.

Supported by neither:

1. adjust_gamma() and adjust_hue() do not support images with transparency.
2. Subpixel translations. #3293

Not supported by torchscript (mostly not possible given the current jit support):

1. single value inputs in Pad(fill), RandomCrop(padding), Resize(size), RandomResizedCrop(size).
2. PIL and Tensor conversions.
3. Compose, RandomOrder, RandomChoice.
4. Lambda.

It is just a draft, let me know if I forget anything. cc @vfdev-5 @datumbox

Start by reviewing torchvision/transforms/transforms.py and torchvision/transforms/functional.py, then compare the listed PIL, Tensor, and TorchScript limitations. The issue is a broad inventory rather than a scoped task, so a contributor would need to select one limitation and define its expected behavior and tests before implementation.