adding alexnet prototype model (#4670)

* adding alexnet prototype model

* adding recipe reference

* fixing lint
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/prototype/models/__init__.py

 from .resnet import *
+from .alexnet import *
 from . import detection
 from . import quantization

torchvision/prototype/models/alexnet.py

+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from ...models.alexnet import AlexNet
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = ["AlexNet", "AlexNetWeights", "alexnet"]
+
+
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+}
+
+
+class AlexNetWeights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
+            "acc@1": 56.522,
+            "acc@5": 79.066,
+        },
+    )
+
+
+def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = AlexNetWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = AlexNetWeights.verify(weights)
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = AlexNet(**kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model