features stands here for out class logits, i.e. you have 160 classes?
If so, you could view the seq_len and batch_size together, in case you want a prediction for each seq timestamp:
x = torch.randn(96, 16, 160)
y = torch.empty(96, 16, dtype=torch.long).random_(160)

criterion = nn.CrossEntropyLoss()
loss = criterion(x.view(-1, 160), y.view(-1))