Hi, for calculating my loss function, I’m using torch.squeeze to change the dimension of the output from [N,1,H,W] to [N,H,W]. I was wondering if this action breaks the computation graoh and cause failure of autograd?

No, `squeeze`

won’t detach the tensor and Autograd will properly calculate the gradients for the original tensor as seen here:

```
x = torch.randn(2, 1, 2, 2, requires_grad=True)
y = x.squeeze(1)
y.mean().backward()
print(x.grad)
```

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