FIx downstream impact by the replacement of torch.load in ultralytics package (#13623)

* Update torch_load

* Auto-format by https://ultralytics.com/actions

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

hubconf.py

@@ -10,7 +10,7 @@ Usage:
     model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')  # local repo
 """
 
-import torch
+from ultralytics.utils.patches import torch_load
 
 
 def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
@@ -87,7 +87,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
             cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0]  # model.yaml path
             model = DetectionModel(cfg, channels, classes)  # create model
             if pretrained:
-                ckpt = torch.load(attempt_download(path), map_location=device)  # load
+                ckpt = torch_load(attempt_download(path), map_location=device)  # load
                 csd = ckpt["model"].float().state_dict()  # checkpoint state_dict as FP32
                 csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"])  # intersect
                 model.load_state_dict(csd, strict=False)  # load