chore: updates

Signed-off-by: Dheeraj Peri <[email protected]>

Author: peri044

docsrc/user_guide/saving_models.rst

@@ -31,7 +31,7 @@ The following code illustrates this approach.
     model = MyModel().eval().cuda()
     inputs = [torch.randn((1, 3, 224, 224)).cuda()]
     trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
-    trt_traced_model = torchtrt.dynamo.export(trt_gm, inputs)
+    trt_traced_model = torch.jit.trace(trt_gm, inputs)
     torch.jit.save(trt_traced_model, "trt_model.ts")
 
     # Later, you can load it and run inference