Update and Add links to the symbolic opset files (onnx#124)

Hi, The original link to symbolic.py is no longer working since now there are multiple files for definitions of symbolic operator functions. Perhaps providing the links to the helper file and to the new opset files is the best fix? I also add the link to torch/csrc/autograd/generated/VariableType.h, for the sake of convenience.

Author: MAXEE998

tutorials/PytorchAddExportSupport.md

@@ -22,12 +22,12 @@ importer (such as onnx-caffe2) developers, as additional work is required.
 ### How to add support to export an operator in PyTorch
 #### Condition 1: If the operator in PyTorch is an ATen operator...
 To determine whether the operator is an ATen operator or not, check
-`torch/csrc/autograd/generated/VariableType.h` (available within generated code in the PyTorch install dir). If you find the corresponding function in this header file, it's most likely an ATen operator.
+[`torch/csrc/autograd/generated/VariableType.h`](https://bddppq.github.io/codebrowser/pytorch/pytorch/torch/csrc/autograd/generated/VariableType.h) (available within generated code in the PyTorch install dir). If you find the corresponding function in this header file, it's most likely an ATen operator.
 
 **Define symbolic functions.** In this case, you should obey the following rules.
-- Define the symbolic function in [`torch/onnx/symbolic.py`](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic.py). Make sure the
+- Define the symbolic function in [`torch/onnx/symbolic.py`](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_helper.py). Make sure the
 function has the same name as the ATen operator/function defined in
-`VariableType.h`.
+[`VariableType.h`](https://bddppq.github.io/codebrowser/pytorch/pytorch/torch/csrc/autograd/generated/VariableType.h).
 - The first parameter is always the exported ONNX graph.
 - Parameter names must match the names in `VariableType.h` EXACTLY, because
 dispatch is done with keyword arguments.
@@ -138,7 +138,10 @@ getting it into ONNX proper, you can add your operator as an *experimental*
 operator.
 
 ## More ONNX symbolic examples
-- [ATen operators in symbolic.py](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic.py)
+- ATen operators in symbolic.py
+ - [symbolic_opset10.py](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_opset10.py)
+ - [symbolic_opset9.py](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_opset9.py)
+ - [symbolic_helper.py](https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_helper.py)
 - [Index](https://github.com/pytorch/pytorch/blob/99037d627da68cdf53d3d0315deceddfadf03bba/torch/autograd/_functions/tensor.py#L24)
 - [Negate](https://github.com/pytorch/pytorch/blob/99037d627da68cdf53d3d0315deceddfadf03bba/torch/autograd/_functions/basic_ops.py#L50)
 - [ConstantPadNd](https://github.com/pytorch/pytorch/blob/99037d627da68cdf53d3d0315deceddfadf03bba/torch/nn/_functions/padding.py#L8)