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.