With this release, we’re supporting several features that allow users to optimize their static quantization:
   1. Observers: you can customize observer modules which specify how statistics are collected prior to quantization to try out more advanced methods to quantize your data.
   2. Operator fusion: you can fuse multiple operations into a single operation, saving on memory access while also improving the operation’s numerical accuracy.
   3. Per-channel quantization: we can independently quantize weights for each output channel in a convolution/linear layer, which can lead to higher accuracy with almost the same speed.


PyTorch API:

To fuse modules, we have torch.quantization.fuse_modules
Observers are inserted using torch.quantization.prepare
Finally, quantization itself is done using torch.quantization.convert


