Minor post FX merge cleanup

tests/test_models.py

@@ -315,7 +315,7 @@ def test_model_forward_fx(model_name, batch_size):
     Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
     """
     if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX because Torch >= 1.10 and Torchvision >= 0.11 are required")
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
 
     model = create_model(model_name, pretrained=False)
     model.eval()
@@ -360,7 +360,7 @@ def test_model_forward_fx(model_name, batch_size):
 def test_model_backward_fx(model_name, batch_size):
     """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
     if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX because Torch >= 1.10 and Torchvision >= 0.11 are required")
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
 
     input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
     if max(input_size) > MAX_BWD_SIZE:
@@ -421,7 +421,7 @@ EXCLUDE_FX_JIT_FILTERS = [
 def test_model_forward_fx_torchscript(model_name, batch_size):
     """Symbolically trace each model, script it, and run single forward pass"""
     if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX because Torch >= 1.10 and Torchvision >= 0.11 are required")
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
 
     input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
     if max(input_size) > MAX_JIT_SIZE:

timm/models/fx_features.py

@@ -71,4 +71,3 @@ class FeatureGraphNet(nn.Module):
 
     def forward(self, x):
         return list(self.graph_module(x).values())
-