Replace self.skip_add.add with torch.add
class InvertedResidual(nn.Module):
def init(self,in_channel,out_channel,stride,expand_ratio):
super(InvertedResidual, self).init()
hidden_channel=int(round(in_channel*expand_ratio))
self.shortcut=stride==1 and in_channel==out_channel
    layers=[]
    if expand_ratio!=1:
        #1x1 pointwise conv
        layers.append(ConvBnRelu(in_channel,hidden_channel,kersize=1))
    layers.extend([
        # 3x3 depthwise conv
        ConvBnRelu(hidden_channel,hidden_channel,stride=stride,groups=hidden_channel),
        nn.Conv2d(hidden_channel,out_channel,kernel_size=1,bias=False),
        nn.BatchNorm2d(out_channel),
    ])

    self.conv=nn.Sequential(*layers)
    #self.skip_add = nn.quantized.FloatFunctional()

def forward(self,x):
    if self.shortcut:
        #return self.skip_add.add(x,self.conv(x))
        return torch.add(x,self.conv(x))
    else:
        return self.conv(x)

RuntimeError: Could not run ‘aten::add.Tensor’ with arguments from the ‘QuantizedCPUTensorId’ backend. ‘aten::add.Tensor’ is only available for these backends: [CPUTensorId, MkldnnCPUTensorId, SparseCPUTensorId, VariableTensorId].
Where did I write it wrong? thanks!