For example, in the end-to-end example, we load in a pre-trained model as qat_model, then we simply perform quantization-aware training using:
```python
   # specify quantization config for QAT
   qat_model.qconfig=torch.quantization.get_default_qat_qconfig('fbgemm')
# prepare QAT
   torch.quantization.prepare_qat(qat_model, inplace=True)
# convert to quantized version, removing dropout, to check for accuracy on each
   epochquantized_model=torch.quantization.convert(qat_model.eval(), inplace=False)
   ```
Device and Operator Support
Quantization support is restricted to a subset of available operators, depending on the method being used, for a list of supported operators, please see the documentation at https://pytorch.org/docs/stable/quantization.html.