RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension for v2 transforms

🐛 Describe the bug

It seems that v2.Pad does not support cases where the padding size is greater than the image size, but v1.Pad does support this. I hope that v2.Pad will allow this in the future as well.

from torchvision.transforms import v2
import torchvision.transforms as T
from torchvision.transforms import functional as F

orig_img = torch.rand([3,32,32])
orig_img = F.to_pil_image(orig_img)

# Not supported
trans_img = v2.Compose([v2.ToImage(), T.Pad(padding=36, padding_mode='reflect')])(orig_img)

# Supported
trans_img = T.Compose([T.Pad(padding=36, padding_mode='reflect')])(orig_img)

Versions

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 546.80
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
...
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchmetrics              1.4.0.post0              pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi

Start with the v2.Pad implementation and its reflect-padding path, then compare the reported v2.Compose reproduction with the working v1.Pad example. Add a regression test covering padding larger than the input dimension and confirm that the v2 case matches the expected supported behavior.