Hi,

Suppose I want to ensemble two models, I find two method, one is like this:

```
model1 = torchvision.models.resnet50().cuda()
model2 = torchvision.models.resnet101().cuda()
inten = torch.randn(16, 3, 224, 224).cuda()
n_iter = 1000
for i in range(n_iter):
out = model1(inten).softmax(1)
out += model2(inten).softmax(1)
```

And another method is to define these two models as children of a `nn.Module`

:

```
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.model1 = torchvision.models.resnet50().cuda()
self.model2 = torchvision.models.resnet101().cuda()
def forward(self, x):
out = self.model1(x).softmax(1)
out += self.model2(x).softmax(1)
return out
model = Model().cuda()
for i in range(n_iter):
out = model(inten)
```

Which one is faster and better optimized during inference ? Will there be difference in terms of speed and memory usage between these two methods ?