* Update test_lstm_impl.py

* Added PackedSequence functionality

* Refactored forward implementation

* Introduce a modular, Python-level implementation of LSTM/LSTMCell
  using existing PyTorch nn.Modules as building blocks
* This allows quantization of weights and internal activations of
  LSTM layers using the existing Quantizer. 
  (In the PyTorch implementation of RNN/LSTM only the weights are 
  exposed at the Python level, whereas the internal activations are 
  "hidden" in C++ code.)
* Supports stacked (multi-layer) and bi-directional LSTM
* Implemented conversion functions from PyTorch LSTM module to
  our LSTM module and vice-versa
* Tests for modular implementation correctness and for conversions
* Jupyter notebook showing post-training quantization of a language
  model