chatbot_tutorial.py: Solve the optimizer cuda call problem

If you don't configure this string of code, you will get an error when you iterate over the update from 4000_checkpoint.tar:

```
encoder_optimizer.step()  
```


Error message:

```
exp_avg.mul_(beta1).add_(1 - beta1, grad)
RuntimeError: expected backend CPU and dtype Float but got backend CUDA and dtype Float
```


Fix it: pytorch/pytorch#2830

```
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)  # missing line from original code
        labels = labels.to(device)  # missing line from original code
        images = images.reshape(-1, 28 * 28)
        out = model(images)
        _, predicted = torch.max(out.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
```

Author: jiangzhonglian

beginner_source/chatbot_tutorial.py

@@ -1326,6 +1326,17 @@ def evaluateInput(encoder, decoder, searcher, voc):
 print('Building optimizers ...')
 encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
 decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)
+# If you have cuda, configure cuda to call
+for state in encoder_optimizer.state.values():
+    for k, v in state.items():
+        if isinstance(v, torch.Tensor):
+            state[k] = v.cuda()
+
+for state in decoder_optimizer.state.values():
+    for k, v in state.items():
+        if isinstance(v, torch.Tensor):
+            state[k] = v.cuda()
+
 if loadFilename:
     encoder_optimizer.load_state_dict(encoder_optimizer_sd)
     decoder_optimizer.load_state_dict(decoder_optimizer_sd)