add TNet tutorial; support converting global transf from TNet format

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diff --git a/tutorial/howto_pretrain_from_tnet.rst b/tutorial/howto_pretrain_from_tnet.rst
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+How to Use a Pre-trained Model from TNet
+========================================
+
+:author: Ted Yin (mfy43) <[email protected]>
+:abstract: Instruct on how to convert a pre-trained TNet model to NERV format,
+           train the converted model and finally convert back to TNet format
+           for subsequent decoding.
+
+- Note: this tutorial is the counterpart to "Plan B" of decoding in *How to Use
+  a Pre-trained nnet Model from Kaldi*. For more complete information, please
+  refer to that tutorial.
+
+- Note: in this tutorial, we use the following notations to denote the directory prefix:
+
+  - ``<nerv_home>``: the path of NERV (the location of outer most directory ``nerv``)
+
+- To convert a TNet DNN model file:
+
+  ::
+    # compile the tool written in C++:
+    g++ -o tnet_to_nerv <nerv_home>/speech/htk_io/tools/tnet_to_nerv.cpp
+    # conver the model (the third argument indicates the initial number used in naming the parameters)
+    ./tnet_to_nerv <path_to_tnet_nn>.nnet <path_to_converted>.nerv 0
+
+- Apply the method above to convert your global transformation file and network
+  file to NERV chunk files respectively.
+
+- Train the converted parameters. Here, a network configuration file similar to
+  the one used in Kaldi tutorial could be found at
+  ``<nerv_home>/nerv/examples/swb_baseline2.lua``.
+
+- Create a copy of ``<nerv_home>/speech/htk_io/tools/nerv_to_tnet.lua``.
+
+    - Modify the list named ``lnames`` to list the name of layers you want to
+      put into the output TNet parameter file in order. You may ask why the
+      NERV-to-TNet converstion is so cumbersome. This is because TNet nnet is a
+      special case of more general NERV toolkit -- it only allows stacked DNNs
+      and therefore TNet-to-NERV conversion is lossless but the other direction
+      is not. Your future NERV network may have multiple branches and that's
+      why you need to specify how to select and "stack" your layers in the TNet
+      parameter output.
+
+    - Do the conversion by:
+
+      ::
+
+         <nerv_home>/install/bin/nerv --use-cpu nerv_to_tnet.lua <your_network_config>.lua <your_trained_params>.nerv <path_to_converted>.nnet
+