message: chore: update doc for partitioning
diff: # TRTorch Partitioning
  -TRTorch partitioning phase is developed to support automatic fallback feature in TRTorch. This phase won't run by
  +TRTorch partitioning phase is developed to support `automatic fallback` feature in TRTorch. This phase won't run by
  default until the automatic fallback feature is enabled.
  On a high level, TRTorch partitioning phase does the following:
  @@ -15,6 +15,8 @@ from the user. Shapes can be calculated by running the graphs with JIT.
  it's still a phase in our partitioning process.
  - `Stitching`. Stitch all TensorRT engines with PyTorch nodes altogether.
  +Test cases for each of these components could be found [here](https://github.com/NVIDIA/TRTorch/tree/master/tests/core/partitioning).
  +
  Here is the brief description of functionalities of each file:
  - `PartitionInfo.h/cpp`: The automatic fallback APIs that is used for partitioning.
  - `SegmentedBlock.h/cpp`: The main data structures that is used to maintain information for each segments after segmentation.
  @@ -34,8 +36,8 @@ To enable automatic fallback feature, you can set following attributes in Python
  ...
  "torch_fallback" : {
  "enabled" : True,
  - "min_block_size" : 1,
  - "forced_fallback_ops": ["aten::foo"],
  + "min_block_size" : 3,
  + "forced_fallback_ops": ["aten::add"],
  }
  })
  ```
  @@ -58,8 +60,8 @@ auto mod = trtorch::jit::load("trt_ts_module.ts");
  auto input_sizes = std::vector<trtorch::CompileSpec::InputRange>{{in.sizes()}};
  trtorch::CompileSpec cfg(input_sizes);
  cfg.torch_fallback = trtorch::CompileSpec::TorchFallback(true);
  -cfg.torch_fallback.min_block_size = 1;
  -cfg.torch_fallback.forced_fallback_ops.push_back("aten::foo");
  +cfg.torch_fallback.min_block_size = 2;
  +cfg.torch_fallback.forced_fallback_ops.push_back("aten::relu");
  auto trt_mod = trtorch::CompileGraph(mod, cfg);
  auto out = trt_mod.forward({in});
  ```
  