predictions = efficientnet(x)
See the full list of new models on the [torchvision.models](https://pytorch.org/vision/master/models.html) documentation page.

We would like to thank Ross Wightman and Luke Melas-Kyriazi for contributing the weights of the EfficientNet variants.

### (Beta) FX-based Feature Extraction 
A new Feature Extraction method has been added to our utilities. It uses [torch.fx](https://pytorch.org/docs/stable/fx.html) and enables us to retrieve the outputs of intermediate layers of a network which is useful for feature extraction and visualization. 

Here is an example of how to use the new utility:

```python
import torch
from torchvision.models import resnet50
from torchvision.models.feature_extraction import create_feature_extractor


x = torch.rand(1, 3, 224, 224)

model = resnet50()

return_nodes = {
"layer4.2.relu_2": "layer4"
}
model2 = create_feature_extractor(model, return_nodes=return_nodes)
intermediate_outputs = model2(x)

print(intermediate_outputs['layer4'].shape)
