for name, module in model.named_modules():
...     if name.endswith("_input_quantizer"):
...         module.load_calib_amax()
...         module.enable_quant()
If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process
model.cuda()
Keep running the quantized model
...
```
Export to ONNX
The goal of exporting to ONNX is to deploy inference by TensorRT. Fake
quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow
the instructions in torch.onnx. Example:
```python
from pytorch_quantization.nn import TensorQuantizer
TensorQuantizer.use_fb_fake_quant = True
Load the calibrated model
