Here is the froward() method of a loss class that calculates loss based on the elements of the confusion matrix. `c_loss`

is float and is being converted to a tensor. This line results in the following warning:

`UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). loss = torch.tensor(c_loss, requires_grad=True)`

The warning makes no sense as `c_loss`

is not a tensor. What am I doing wrong here?

```
def forward(self, inputs: torch.Tensor, targets: torch.Tensor):
_, predictions = torch.max(inputs, 1)
c = confusion_matrix(targets.cpu().numpy(), predictions.cpu().numpy())
c_loss = c[0, 1] / (c[0, 1] + c[1, 1])
# this line results in the warning
loss = torch.tensor(c_loss, requires_grad=True)
return loss
```