print(k, v)

Additionally, each weights entry is associated with the necessary preprocessing transforms. All current preprocessing transforms are JIT-scriptable and can be accessed via the `transforms` attribute. Prior using them with the data, the transforms need to be initialized/constructed. This lazy initialization scheme is done to ensure the solution is memory efficient. The input of the transforms can be either a `PIL.Image` or a `Tensor` read using `torchvision.io`.

```Python
from torchvision.prototype.models import ResNet50_Weights

# Initializing preprocessing at standard 224x224 resolution
preprocess = ResNet50_Weights.IMAGENET1K_V2.transforms()

# Initializing preprocessing at 400x400 resolution
preprocess = ResNet50_Weights.IMAGENET1K_V2.transforms(crop_size=400, resize_size=400)

# Once initialized the callable can accept the image data:
# img_preprocessed = preprocess(img)
