Loss Tensors Are Saved Twice On AWS Pytorch

loss functional values are saved twice for each step with AWS Pytorch.

This happens because functional losses are saved by default by the post_hook_for_loss_functional fn in AWS Pytorch.

```
...
for _ in range(n_steps):
optimizer.zero_grad()
outputs = net(inputs)
loss = F.cross_entropy(outputs, labels)
hook.record_tensor_value("nll_loss", tensor_value=loss)
loss.backward()
optimizer.step()
...
```

The `post_hook_for_loss_functional` is called by `F.cross_entropy(outputs, labels)`