able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are
more difficult to quantize, such as MobileNets and BERT-large.
Tips:
QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
  inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
QDQBERT requires the dependency of Pytorch Quantization Toolkit. To install pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example bert-base-uncased), and
  perform Quantization Aware Training/Post Training Quantization.
A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
