fix can't instantiate abstract class

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fix

tests/tests_lite/plugins/precision/test_native_amp_integration.py

@@ -74,17 +74,15 @@ def test_native_mixed_precision(accelerator, precision, expected_dtype):
     lite.run()
 
 
-@RunIf(min_torch="1.13", min_cuda_gpus=1)
-def test_native_mixed_precision_fused_optimizer_parity():
-    def run(fused=False):
+class FusedTest(LightningLite):
+    def run(self, fused=False):
         seed_everything(1234)
-        lite = LightningLite(accelerator="cuda", precision=16, devices=1)
 
-        model = nn.Linear(10, 10).to(lite.device)  # TODO: replace with individual setup_model call
+        model = nn.Linear(10, 10).to(self.device)  # TODO: replace with individual setup_model call
         optimizer = torch.optim.Adam(model.parameters(), lr=1.0, fused=fused)
 
-        model, optimizer = lite.setup(model, optimizer)
-        assert isinstance(lite._precision.scaler, torch.cuda.amp.GradScaler)
+        model, optimizer = self.setup(model, optimizer)
+        assert isinstance(self._precision.scaler, torch.cuda.amp.GradScaler)
 
         data = torch.randn(10, 10, device="cuda")
         target = torch.randn(10, 10, device="cuda")
@@ -94,13 +92,18 @@ def run(fused=False):
             optimizer.zero_grad()
             output = model(data)
             loss = (output - target).abs().sum()
-            lite.backward(loss)
+            self.backward(loss)
             optimizer.step()
             losses.append(loss.detach())
         return torch.stack(losses), model.parameters()
 
-    losses, params = run(fused=False)
-    losses_fused, params_fused = run(fused=True)
+
+@RunIf(min_torch="1.13", min_cuda_gpus=1)
+def test_native_mixed_precision_fused_optimizer_parity():
+    lite = FusedTest(accelerator="cuda", precision=16, devices=1)
+    losses, params = lite.run(fused=False)
+    lite = FusedTest(accelerator="cuda", precision=16, devices=1)
+    losses_fused, params_fused = lite.run(fused=True)
 
     # Both the regular and the fused version of Adam produce the same losses and model weights
     torch.testing.assert_close(losses, losses_fused)