When I read the code in this

```
class MultiLabelDemo(nn.Module):
def __init__(self):
super(MultiLabelDemo, self).__init__()
self.main_block = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=96, out_channels=96, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=96, out_channels=96, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
)
self.tail_block1 = nn.Sequential(
nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, stride=1, padding=2),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
)
self.tail_block2 = nn.Sequential(
nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, stride=1, padding=2),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
)
def forward(self, data):
x = self.main_block(data)
y1 = self.tail_block1(x)
y2 = self.tail_block2(x)
return y1, y2
```

When I have multiple branches, is it the same if I use `clone()`

to one of them? or all of them?

```
def forward(self, data):
x = self.main_block(data)
y1 = self.tail_block1(x)
y2 = self.tail_block2(x**.clone()**)
y3 = self.tail_block3(x**.clone()**)
y1 = y1 * (y2+y3)
return y1
```

In my code, I found if I dont use colone, I will have the error

```
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation.
```