Passing the parameters of pretrained googlenet to an optimizer

I downloaded the pretrained quantized googlenet model to finetune on some data.

model = models.quantization.googlenet(pretrained=True, quantize=True)

However, list(model.parameters()) yields [].

How can I access the parameters to pass to an optimizer or set requires_grad?

There is no clear structure in the model containing the parameters, like ModuleList or something, which is mentioned in other posts about this type of topic. Here is a fragment of the model:

(conv1): QuantizableBasicConv2d(
(conv): QuantizedConvReLU2d(3, 64, kernel_size=(7, 7), stride=(2, 2), scale=0.08655554801225662, zero_point=0, padding=(3, 3))
(bn): Identity()
(relu): Identity()


From the docs, it seems that quantized models aren’t intended to be user with an optimizer (I’m assuming for training):