Net: Alexnet
Dataset: Imagenet
Quantization: Post-training Static Quantization with ‘fbgemm’.
The net:
import os
import sys
import random

import torch
import torch.nn as nn
import torchvision
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms

from utils import load_state_dict_from_url

import collections

######## AlexNet model ########

__all__ = ['AlexNet', 'alexnet']

model_urls = { 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth', }

class AlexNet(nn.Module):

    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()

        self.features = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(kernel_size=3, stride=2),
                nn.Conv2d(64, 192, kernel_size=5, padding=2),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(kernel_size=3, stride=2),
                nn.Conv2d(192, 384, kernel_size=3, padding=1),
                nn.ReLU(inplace=True),
                nn.Conv2d(384, 256, kernel_size=3, padding=1),
                nn.ReLU(inplace=True),
                nn.Conv2d(256, 256, kernel_size=3, padding=1),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(kernel_size=3, stride=2),
                )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
                nn.Dropout(),
                nn.Linear(256 * 6 * 6, 4096),
                nn.ReLU(inplace=True),
                nn.Dropout(),
                nn.Linear(4096, 4096),
                nn.ReLU(inplace=True),
                nn.Linear(4096, num_classes),
                )
        # QuantStub converts tensors from floating point to quantized
        self.quant = torch.quantization.QuantStub()
        # DeQuantStub converts tensors from quantized to floating point
        self.dequant = torch.quantization.DeQuantStub()

    def forward(self, x):
        # manually specify where tensors will be converted from floating
        # point to quantized in the quantized model
        x = self.quant(x)
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        # manually specify where tensors will be converted from floating
        # point to quantized in the quantized model
        x = self.quant(x)
        return x


def alexnet(pretrained=False, progress=True, **kwargs):
    r"""AlexNet model architecture from the
    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = AlexNet(**kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls['alexnet'], progress=progress)
        model.load_state_dict(state_dict)
    return model

However, I get the following error:
RuntimeError: Could not run 'aten::quantize_per_tensor' with arguments from the 'QuantizedCPU' backend. 'aten::quantize_per_tensor' is only available for these backends: [CPU, CUDA, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].

How to resolve this?
Thanks.