[Doc] Tutorials re-organization (#2683)

* reorg

* change titles

* rm some stale API doc; minor fix

* fix docs

* add warning

* rm new-tutorial run in ci

* lint

tutorials/models/2_small_graph/3_tree-lstm.py

 """
 .. _model-tree-lstm:
 
-Tutorial: Tree-LSTM in DGL
+Tree-LSTM in DGL
 ==========================
 
 **Author**: Zihao Ye, Qipeng Guo, `Minjie Wang
 <https://jermainewang.github.io/>`_, `Jake Zhao
 <https://cs.nyu.edu/~jakezhao/>`_, Zheng Zhang
+
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 """
  
 ##############################################################################

tutorials/models/2_small_graph/README.txt

 .. _tutorials2-index:
 
 Batching many small graphs
-==============================
+-------------------------------
 
 * **Tree-LSTM** `[paper] <https://arxiv.org/abs/1503.00075>`__ `[tutorial]
   <2_small_graph/3_tree-lstm.html>`__ `[PyTorch code]

tutorials/models/3_generative_model/5_dgmg.py

 """
 .. _model-dgmg:
 
-Tutorial: Generative models of graphs
+Generative Models of Graphs
 ===========================================
 
 **Author**: `Mufei Li <https://github.com/mufeili>`_,
 `Lingfan Yu <https://github.com/ylfdq1118>`_, Zheng Zhang
+
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 """
 
 ##############################################################################

tutorials/models/3_generative_model/README.txt

 .. _tutorials3-index:
 
 Generative models
-==================
+--------------------
 
 * **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]
   <3_generative_model/5_dgmg.html>`__ `[PyTorch code]
@@ -12,10 +12,3 @@ Generative models
   sample has a dynamic, probability-driven structure that is not available
   before training. You can progressively leverage intra- and
   inter-graph parallelism to steadily improve the performance.
-
-* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[PyTorch code]
-  <https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn>`__:
-  This network generates molecular graphs using the framework of
-  a variational auto-encoder. The junction tree neural network (JTNN) builds
-  structure hierarchically. In the case of molecular graphs, it uses a junction tree as
-  the middle scaffolding.

tutorials/models/4_old_wines/2_capsule.py

 """
 .. _model-capsule:
 
-Capsule network tutorial
+Capsule Network
 ===========================
 
 **Author**: Jinjing Zhou, `Jake Zhao <https://cs.nyu.edu/~jakezhao/>`_, Zheng Zhang, Jinyang Li
@@ -9,6 +9,14 @@ Capsule network tutorial
 In this tutorial, you learn how to describe one of the more classical models in terms of graphs. The approach
 offers a different perspective. The tutorial describes how to implement a Capsule model for the
 `capsule network <http://arxiv.org/abs/1710.09829>`__.
+
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 """
 #######################################################################################
 # Key ideas of Capsule

tutorials/models/4_old_wines/7_transformer.py

 """
 .. _model-transformer:
 
-Transformer tutorial
-====================
+Transformer as a Graph Neural Network
+======================================
 
 **Author**: Zihao Ye, Jinjing Zhou, Qipeng Guo, Quan Gan, Zheng Zhang
+
+.. warning::
+
+    The tutorial aims at gaining insights into the paper, with code as a mean
+    of explanation. The implementation thus is NOT optimized for running
+    efficiency. For recommended implementation, please refer to the `official
+    examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
+
 """
 ################################################################################################
 # In this tutorial, you learn about a simplified implementation of the Transformer model.

tutorials/models/4_old_wines/README.txt

@@ -2,7 +2,7 @@
 
 
 Revisit classic models from a graph perspective
-====================================
+-------------------------------------------------------
 
 * **Capsule** `[paper] <https://arxiv.org/abs/1710.09829>`__ `[tutorial]
   <4_old_wines/2_capsule.html>`__ `[PyTorch code]

tutorials/models/README.txt

+Paper Study with DGL
+=========================================