Allow image model (color conversion) to be specified as part of datasets Image() decode
### Feature request

Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step.

datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)):
```
from torchvision.transforms import Compose, ColorJitter, ToTensor

jitter = Compose(
    [
         ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7),
         ToTensor(),
    ]
)

def transforms(examples):
    examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]]
    return examples
```




### Motivation

It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines...

### Your contribution

Can do a PR with guidance on how mode should be passed / set on the dataset.