Replace deprecated arg in torchvision models (#6401)

### Description

For torchvision models, the parameter `pretrained` is deprecated since
0.13 and may be removed in the future. Our `TorchVisionFC` model has
been using this parameter. This PR updated `TorchVisionFC` to remove
calling pretrained and instead to call `weights` with appropriate
defaults without any change in the functionality (backward compatible).

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [x] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.

---------

Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com>
Signed-off-by: Wenqi Li <wenqil@nvidia.com>
Co-authored-by: Nic Ma <nma@nvidia.com>
Co-authored-by: Wenqi Li <wenqil@nvidia.com>

monai/networks/nets/torchvision_fc.py

@@ -114,8 +114,10 @@ def __init__(
     ):
         if weights is not None:
             model = getattr(models, model_name)(weights=weights, **kwargs)
+        elif pretrained:
+            model = getattr(models, model_name)(weights="DEFAULT", **kwargs)
         else:
-            model = getattr(models, model_name)(pretrained=pretrained, **kwargs)  # 'pretrained' deprecated 0.13
+            model = getattr(models, model_name)(weights=None, **kwargs)
 
         super().__init__(
             model=model,

tests/test_dynunet.py

@@ -19,8 +19,11 @@
 
 from monai.networks import eval_mode
 from monai.networks.nets import DynUNet
+from monai.utils import optional_import
 from tests.utils import assert_allclose, skip_if_no_cuda, skip_if_windows, test_script_save
 
+InstanceNorm3dNVFuser, _ = optional_import("apex.normalization", name="InstanceNorm3dNVFuser")
+
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 strides: Sequence[Sequence[int] | int]
@@ -125,6 +128,15 @@ def test_script(self):
 @skip_if_no_cuda
 @skip_if_windows
 class TestDynUNetWithInstanceNorm3dNVFuser(unittest.TestCase):
+    def setUp(self):
+        try:
+            layer = InstanceNorm3dNVFuser(num_features=1, affine=False).to("cuda:0")
+            inp = torch.randn([1, 1, 1, 1, 1]).to("cuda:0")
+            out = layer(inp)
+            del inp, out, layer
+        except Exception:
+            self.skipTest("NVFuser not available")
+
     @parameterized.expand([TEST_CASE_DYNUNET_3D[0]])
     def test_consistency(self, input_param, input_shape, _):
         for eps in [1e-4, 1e-5]: