chore: import upstream snapshot with attribution

docs/source/guide/nn.rst

@@ -0,0 +1,39 @@
+.. _guide-nn:
+
+Chapter 3: Building GNN Modules
+===============================
+
+:ref:`(中文版) <guide_cn-nn>`
+
+DGL NN module consists of building blocks for GNN models. An NN module inherits
+from `Pytorch’s NN Module <https://pytorch.org/docs/1.2.0/_modules/torch/nn/modules/module.html>`__, `MXNet Gluon’s NN Block  <http://mxnet.incubator.apache.org/versions/1.6/api/python/docs/api/gluon/nn/index.html>`__ and `TensorFlow’s Keras
+Layer <https://www.tensorflow.org/api_docs/python/tf/keras/layers>`__, depending on the DNN framework backend in use. In a DGL NN
+module, the parameter registration in construction function and tensor
+operation in forward function are the same with the backend framework.
+In this way, DGL code can be seamlessly integrated into the backend
+framework code. The major difference lies in the message passing
+operations that are unique in DGL.
+
+DGL has integrated many commonly used
+:ref:`apinn-pytorch-conv`, :ref:`apinn-pytorch-dense-conv`, :ref:`apinn-pytorch-pooling`,
+and
+:ref:`apinn-pytorch-util`. We welcome your contribution!
+
+This chapter takes :class:`~dgl.nn.pytorch.conv.SAGEConv` with Pytorch backend as an example
+to introduce how to build a custom DGL NN Module.
+
+Roadmap
+-------
+
+* :ref:`guide-nn-construction`
+* :ref:`guide-nn-forward`
+* :ref:`guide-nn-heterograph`
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+    :glob:
+
+    nn-construction
+    nn-forward
+    nn-heterograph