Hi, not sure if you have already solved this but this is because torch supports two different quantization schemes: per tensor affine and per channel affine. In per tensor affine, a single scale and zero point are saved per tensor. So you can use .q_scale() as you did to get that single value.
However, in your case, PyTorch is using per channel affine which means there are N scale and zero point values, where N = number of channels. In this case you have to use `.q_per_channel_scales()’ to return a tensor of all the scale values.