Using legacy - normalization layer

vision
1

I want to use a normalization layer after my fully connected layer.
I saw that there is one in the legacy, but is it possible to use it?

Edit, the following code seems to work:

 def l2_norm(self,input):
    input_size = input.size()
    buffer = torch.pow(input, 2)

    normp = torch.sum(buffer, 1).add_(1e-10)
    norm = torch.sqrt(normp)

    _output = torch.div(input, norm.view(-1, 1).expand_as(input))

    output = _output.view(input_size)

    return output
2

Hi Liors,
I face the same problem. Could you please give some hints if you have already fixed it?
Many thanks.