Hi,

I have this

```
{'model_state_dict': OrderedDict([(u'conv_3x3_32a.weight', tensor([[
[[ 0.0367, 0.0294, -0.1065],
[ 0.0918, 0.1065, -0.0331],
[-0.0147, 0.0184, -0.1028]]],
.......
[[[ 0.1249, 0.0661, -0.0257],
[ 0.0735, -0.0257, -0.1028],
[ 0.0441, -0.0698, -0.0771]]]], size=(40, 1, 3, 3),
dtype=torch.qint8, quantization_scheme=torch.per_tensor_affine,
scale=0.00367316859774, zero_point=0)), (u'conv_3x3_32a.scale', tensor(0.0031)), (u'conv_3x3_32a.zero_point', tensor(160))
```

I understand that the weights tensor has its own scale which is 0.00367316859774, but I have 2 questions:

- Which is the purpose of the layer scale and zero point? Where do I use them?
- How can I find which is the re-quantization scale used after the weight and input multiplication and accumulation? I don’t know how to access it.