Using the Multi-pretrained weight API
We are currently working on a new prototype mechanism which will extend the model builder methods of TorchVision to support multiple weights. Along with the weights, we store useful meta-data (such as the labels, the accuracy, links to recipe etc) and the preprocessing transforms necessary for using the models. Example:
```python
  from PIL import Image
  from torchvision import prototype as P
  img = Image.open("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
   
  # Initialize model
  weights = P.models.ResNet50_Weights.IMAGENET1K_V2
  model = P.models.resnet50(weights=weights)
  model.eval()
# Initialize inference transforms
  preprocess = weights.transforms()
   
  # Apply inference preprocessing transforms
  batch = preprocess(img).unsqueeze(0)