Setting a float value to `magnitude` argument of `RandAugment()` gets the wrong error message

🐛 Describe the bug

Setting a float value to magnitude argument of RandAugment() gets the wrong error message as shown below:

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandAugment

my_data = OxfordIIITPet(
    root="data",
    transform=RandAugment(magnitude=3.5)
)

my_data[0] # Error

IndexError: only integers, slices (:), ellipsis (...), None and long or byte Variables are valid indices (got float)

But setting None to magnitude argument doesn't work as shown below so the wrong error message above should be corrected:

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandAugment

my_data = OxfordIIITPet(
    root="data",
    transform=RandAugment(magnitude=None)
)

my_data[0] # Error

ValueError: only one element tensors can be converted to Python scalars

In addition, setting a list of one element to magnitude argument works as shown below but it's useless so magnitude argument should only accept int but not list(int):

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandAugment

my_data = OxfordIIITPet(
    root="data",
    transform=RandAugment(magnitude=[3])
)

my_data[0]
# (<PIL.Image.Image image mode=RGB size=394x500>, 0)

Versions

import torchvision

torchvision.__version__ # '0.20.1'

Start at the torchvision.transforms.v2.RandAugment entry point and reproduce the reported cases with magnitude=3.5, None, and [3]. Trace where magnitude is consumed, then add or update coverage so invalid non-integer values produce the intended validation error and single-item lists are rejected.