(we use 8-bit values below, but this applies to any bit-width)
* We use the notion of "full" and "restricted" quantized range for
  symmetric quantization (see section 2.2 in https://arxiv.org/abs/1806.08342)
* "Full" quantized range ==> [-128, 127], "restircted" ==> [-127, 127]
* Until now, when doing symmetric quantization we assumed a "full"
  range when saturating after quantization, but calculated the scale
  factor as if the range was restricted. This means we weren't making
  full utilization of the quantized range.
* On the other hand, in some other implementations of quantization (e.g.
  TensorFlow), the "restricted" range is used.
* So, we make it an option to use either the proper "full" range
  (q_min = -128) or "restricted" range (q_min = -127).
* LinearQuantMode.SYMMETRIC now means the "full" range is used, and
  added LinearQuantMode.SYMMETRIC_RESTRICTED for using the "restricted"
  range.
* Updated tests and documentation.

* Added scale factor approximation in post-training quantization using
  integer multiply + shift. # of bits for integer multiplier is user
  configurable
* Updated documentation
* Updated post-train quant command line examples readme file

* Some refactoring to enable multiple clipping methods
* BREAKING: clip_acts as a boolean flag (either in command line
  or in function signature) will fail. Error message with valid
  values from is displayed.
* Implemented clipping activations at mean + N * std
  (N is user configurable)
* Additional tests
* Updated docs

* Bias handling:
  * Add 'bits_bias' parameter to explicitly specify # of bits for bias,
    similar to weights and activations.
  * BREAKING: Remove the now redundant 'quantize_bias' boolean parameter
* Custom overrides:
  * Expand the semantics of the overrides dict to allow overriding of
    other parameters in addition to bit-widths
  * Functions registered in the quantizer's 'replacement_factory' can
    define keyword arguments. Non bit-width entries in the overrides
    dict will be checked against the function signature and passed
  * BREAKING:
    * Changed the name of 'bits_overrides' to simply 'overrides'
    * Bit-width overrides must now be defined using the full parameter
      names - 'bits_activations/weights/bias' instead of the short-hands
      'acts' and 'wts' which were used so far.
  * Added/updated relevant tests
  * Modified all quantization YAMLs under 'examples' to reflect 
    these changes
  * Updated docs

Summary of changes:
(1) Post-train quantization based on pre-collected statistics
(2) Quantized concat, element-wise addition / multiplication and embeddings
(3) Move post-train quantization command line args out of sample code
(4) Configure post-train quantization from YAML for more fine-grained control

(See PR #136 for more detailed changes descriptions)

- 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

* Asymmetric post-training quantization (only symmetric supported so until now)
* Quantization aware training for range-based (min-max) symmetric and asymmetric quantization
* Per-channel quantization support in both training and post-training
* Added tests and examples
* Updated documentation

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

* Adding PACT quantization method
* Move logic modifying the optimizer due to changes the quantizer makes into the Quantizer itself
* Updated documentation and tests

* Scale of bias and parentheses were wrong