tutorial update (#870)

Summary:
Pull Request resolved: #870

seems like we want to be using longterm apis now
D49972005

so updating docs to match such usage

Reviewed By: kirklandsign

Differential Revision: D50235705

fbshipit-source-id: c71b46fff333eac10e938cca922b6a32852d7b5c

Author: mcr229

docs/source/tutorial-xnnpack-delegate-lowering.md

@@ -34,7 +34,7 @@ sample_inputs = (torch.randn(1, 3, 224, 224), )
 edge = export_to_edge(mobilenet_v2, example_inputs)
 
 
-edge.to_backend(XnnpackPartitioner)
+edge = edge.to_backend(XnnpackPartitioner)
 ```
 
 We will go through this example with the MobileNetV2 pretrained model downloaded from the TorchVision library. The flow of lowering a model starts after exporting the model `to_edge`. We call the `to_backend` api with the `XnnpackPartitioner`. The partitioner identifies the subgraphs suitable for XNNPACK backend delegate to consume. Afterwards, the identified subgraphs will be serialized with the XNNPACK Delegate flatbuffer schema and each subgraph will be replaced with a call to the XNNPACK Delegate.
@@ -92,7 +92,7 @@ edge = export_to_edge(
     example_inputs,
     edge_compile_config=EdgeCompileConfig(_check_ir_validity=False)
 )
-edge.to_backend(XnnpackPartitioner)
+edge = edge.to_backend(XnnpackPartitioner)
 
 exec_prog = edge.to_executorch()
 save_pte_program(exec_prog.buffer, "qs8_xnnpack_mobilenetv2.pte")