I was wondering if there is an equivalent for `tf.compat.v1.losses.hinge_loss`

in PyTorch? Is `torch.nn.HingeEmbeddingLoss`

the equivalent function? Thanks!

**Edits:**

I implemented the Hinge Loss function from the definition as below:

```
class HingeLoss(torch.nn.Module):
def __init__(self):
super(HingeLoss, self).__init__()
self.relu = nn.ReLU()
def forward(self, output, target):
all_ones = torch.ones_like(target)
labels = 2 * target - all_ones
losses = all_ones - torch.mul(output.squeeze(1), labels)
return torch.norm(self.relu(losses))
```

However, this gives a different value than `torch.nn.HingeEmbeddingLoss`

, as I have explained in this example.