We have a tutorial with an end-to-end example of quantization (this same tutorial also covers our third quantization method, quantization-aware training), but because of our simple API, the three lines that perform post-training static quantization on the pre-trained model myModel are:
  ```python
   # set quantization config for server (x86)
   deploymentmyModel.qconfig = torch.quantization.get_default_config('fbgemm')
# insert observers
   torch.quantization.prepare(myModel, inplace=True)
   # Calibrate the model and collect statistics
# convert to quantized version
   torch.quantization.convert(myModel, inplace=True)
  ```


Quantization Aware Training

