human
2.2.2 Training the Network
In contrast to classification, where only the last output is used, we
are making a prediction at every step, so we are calculating loss at
every step.
The magic of autograd allows you to simply sum these losses at each step
and call backward at the end.
For the loss function nn.NLLLoss is appropriate, since the last
layer of the RNN is nn.LogSoftmax.
For the different between nn.NLLLoss and the nn.LogSoftmax, we could Look
at the source code, the most import line is quote as follow:
``` python
 def cross_entropy(input, target):
    return nll_loss(log_softmax(input, 1))
```
In a word, Cross entropy combines log_softmax and nll_loss in a single function.
 Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.
criterion = nn.NLLLoss()

learning_rate = 0.0005

def train(category_tensor, input_line_tensor, target_line_tensor):
    target_line_tensor.unsqueeze_(-1)
    hidden = rnn.initHidden(is_cuda)

    rnn.zero_grad()

    loss = 0

    # Take care of the loss function, 
    # it could be visualized as the following figure 
    for i in range(input_line_tensor.size(0)):
        output, hidden = rnn(category_tensor, input_line_tensor[i], hidden)
        l = criterion(output, target_line_tensor[i])
        loss += l

    loss.backward()

    for p in rnn.parameters():
        if hasattr(p.grad, "data"):
            p.data.add_(-learning_rate, p.grad.data)

    return output, loss.item() / input_line_tensor.size(0)