I want to transform a PIL image or np.ndarray, but it in both cases, the transform does nothing to the image.

**Minimal reproducable example:** As you can see, the mean does not change

```
import torch
import numpy as np
import torchvision.transforms.v2 as v2
import matplotlib.pyplot as plt
from PIL import Image
## np.array (does nothing / fails silently)
img_np = np.ones((100,100,3))
img_np_transformed = v2.Pad(50)(img_np)
print(np.mean(img_np_transformed)) # still 1.0
plt.imshow(img_np_transformed)
## PIL image (does nothing / fails silently)
img_pil = Image.fromarray((img_np * 255).astype('uint8'))
img_pil_transformed = v2.Pad(50)(img_np) # still 1.0
print(np.mean(img_pil_transformed))
## Tensor (works)
tensor_img = torch.tensor(np.ones((1,3,100,100)))
tensor_img_transformed = v2.Pad(50)(tensor_img)
print(float(tensor_img_transformed.mean())) # correct 0.25
plt.figure()
plt.imshow(np.transpose(tensor_img_transformed.squeeze(), (2, 1, 0)))
```

What do I do wrong or is this a bug? According to the docs, PIL Images should work. And at least, I would expect an Exception if the input is invalid.