SpeakerDecoder weight normalization
Hi!

When reading SpeakerDecoder code I bumped into [this](https://github.com/NVIDIA/NeMo/blob/1d3eb3d0d045f7f57316217320b8d265bf3256ce/nemo/collections/asr/modules/conv_asr.py#L848) line. Looks like it is weight normalization for AAS loss. But why should it work?

```
>>> import torch.nn as nn
>>> import torch.nn.functional as F
>>> layer = nn.Linear(128, 100, bias=False)
>>> weight1 = layer.weight.data.clone()
>>> for W in layer.parameters():
...     W = F.normalize(W, p=2, dim=1)
...
>>> weight2 = layer.weight.data.clone()
>>> weight1 == weight2
tensor([[True, True, True,  ..., True, True, True],
        [True, True, True,  ..., True, True, True],
        [True, True, True,  ..., True, True, True],
        ...,
        [True, True, True,  ..., True, True, True],
        [True, True, True,  ..., True, True, True],
        [True, True, True,  ..., True, True, True]])
>>>
```
