* "Net-aware quantization" - using the term coined in
  https://arxiv.org/abs/1811.09886. (section 3.2.2).
  Refers to considering sequences of modules when quantizing. This 
  isn't exactly layer fusion - we modify activation stats prior to
  setting quantization parameters, to make sure that when a module
  is followed by certain activation functions, only the relevant
  ranges are quantized. We do this for:
    * ReLU - Clip all negative values
    * Tanh / Sigmoid - Clip according to the (approximated) saturation
      values for these functions. We use [-4, 4] for tanh and [-6, 6]
      for sigmoid.

* Perform batch-norm folding before post-training quantization.
  Batch-norm parameters are folded into the parameters of the previous
  layer and the BN layer is replaced with an identity module.

* Both BN folding and "net-aware" are now automatically executed
  in PostTrainLinearQuantizer (details of this change below)

* BN folding enabled by new generic mechanism to "fuse" module
  sequences (at the Python API level)
    * First module in sequence is replaced/modified by a user-provided
      function, rest of moudles replaced with nn.Identity

* Quantizer changes:
  * Optionally create adjacency map during prepare_model
  * Subclasses may enforce adjacency map creation
  * Refatcoring: Replace _prepare_model_impl with pre and post
    override-able "callbacks", so core functionality is always executed

* PostTrainLinearQuantizer Changes:
  * Enforce creation of adjacency map. This means users must now pass a
    dummy input to PostTrainLinearQuantizer.prepare_model
  * Before module replacement - Apply BN folding and stats updates according
    to net-aware quantization

* Updated the language model quantization tutorial to reflect the new
  functionality

* Updated the image classification post-train quantization samples
  (command line and YAML)

* Other changes:
  * Distller LSTM implementation:
    Replace the ModuleList for cells with a plain list. The PyTorch trace
    mechanism doesn't "see" ModuleList objects, it only sees the 
    contained modules. This means that the "scopeName" of these modules
    isn't complete, which makes it impossible to match op names in 
    SummaryGraph to modules in the Python model.
  * ActivationStatsCollector: Ignore nn.Identity modules

Not backward compatible - re-installation is required

* Fixes for PyTorch==1.0.0
* Refactoring folder structure
* Update installation section in docs

- Moved the Language model and struct pruning tutorials from the Wiki to
the HTML documentation.  Love the ease of Wiki, but GitHub doesn't let
Google crawl these pages, and users can't open PRs on Wiki pages.

- Updated the pruning algorithms documentation

* Implemented as a Policy
* Integrated in image classification sample
* Updated docs and README