Hi,

There seems to be a quantized::add operator but I can’t find how to use it

```
class QuantAdd(torch.nn.Module):
def __init__(self):
super(QuantAdd, self).__init__()
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()
def forward(self, x, y):
x = self.quant(x)
y = self.quant(y)
out = x + y
return self.dequant(out)
model = QuantAdd()
torch.backends.quantized.engine = 'qnnpack'
model.qconfig = torch.quantization.get_default_qconfig('qnnpack')
torch.quantization.prepare(model, inplace=True)
a = torch.rand(1, 3, 4, 4)
b = torch.rand(1, 3, 4, 4)
_ = model(a, b)
torch.quantization.convert(model, inplace=True)
traced_model = torch.jit.trace(model, (a, b))
```

The above will result in the error:

```
NotImplementedError: Could not run 'aten::empty.memory_format' with arguments from the 'QuantizedCPU' backend.
```

I tried using `out.copy_(x + y)`

on a pre-allocated tensor but still get the ‘aten::empty.memory_format’ error which I guess is related to x + y return. So I tried in-place addition (both with `x += y`

and `x.add_(y)`

) and I get:

```
NotImplementedError: Could not run 'aten::add.out' with arguments from the 'QuantizedCPU' backend.
```

Am I missing something obvious? What’s the right way to use quantized addition in a model?

Thanks,

Julien