# Copyright 2016    Johns Hopkins University (Dan Povey)
  #           2016    Vijayaditya Peddinti
  #           2016    Yiming Wang
  # Apache 2.0.
  
  """This library has classes and methods to form neural network computation graphs,
  in the nnet3 framework, using higher level abstractions called 'layers'
  (e.g. sub-graphs like LSTMS ).
  
  Note : We use the term 'layer' though the computation graph can have a highly
  non-linear structure as, other terms such as nodes/components have already been
  used in C++ codebase of nnet3.
  
  This is basically a config parser module, where the configs have very concise
  descriptions of a neural network.
  
  This module has methods to convert the xconfigs into a configs interpretable by
  nnet3 C++ library.
  
  It generates three different configs:
   'init.config' : which is the config with the info necessary for computing
                 the preconditioning matrix i.e., LDA transform
                 e.g.
                   input-node name=input dim=40
                   input-node name=ivector dim=100
                   output-node name=output input=Append(Offset(input, -2), Offset(input, -1), input, Offset(input, 1), Offset(input, 2), ReplaceIndex(ivector, t, 0)) objective=linear
  
   'ref.config' : which is a version of the config file used to generate
                  a model for getting left and right context (it doesn't read
                  anything for the LDA-like transform and/or
                  presoftmax-prior-scale components)
  
   'final.config' : which has the actual config used to initialize the model used
                   in training i.e, it has file paths for LDA transform and
                   other initialization files
  """
  
  
  __all__ = ["utils", "layers", "parser"]