Notes

I apologize for the oversight. It seems like there's a discrepancy in the sequence lengths while generating predictions. This is likely because the model's maximum sequence length and the actual generated sequence length don't match.

To address this issue, let's adjust the sequence length while generating predictions:

```python
import torch
import nltk

# Sample input text
input_text = "What is the weather today?"

# Tokenization and adding special tokens
input_tokens = nltk.word_tokenize(input_text.lower())
input_tokens = [START_TOKEN] + input_tokens + [END_TOKEN]

# Converting tokens to indices
input_indices = [combined_to_index[token] for token in input_tokens]

# Padding and reshaping
max_input_length = max_sequence_length # Choose an appropriate length
padded_input = input_indices + [combined_to_index[PADDING_TOKEN]] * (max_input_length - len(input_indices))
input_tensor = torch.tensor(padded_input).unsqueeze(0) # Add a batch dimension

# Move the input tensor to the appropriate device (GPU if available)
input_tensor = input_tensor.to(device)

# Pass through the model for prediction
with torch.no_grad():
model.eval() # Set the model to evaluation mode
predicted_indices = []

# Generate output sequence token by token
for _ in range(max_sequence_length):
output = model(input_tensor, input_tensor) # Use input_tensor as both src and tgt
new_token_index = output[0, -1, :].argmax().item() # Get the index of the predicted token
predicted_indices.append(new_token_index)

if new_token_index == combined_to_index[END_TOKEN]:
break

# Update input tensor for next iteration
input_tensor = torch.cat([input_tensor, torch.tensor([[new_token_index]]).to(device)], dim=1)

# Stop generating if the predicted sequence length exceeds the model's max_sequence_length
if input_tensor.size(1) >= max_sequence_length:
break

# Convert predicted indices back to tokens
predicted_tokens = [index_to_combined[idx] for idx in predicted_indices]

# Remove special tokens and padding tokens
predicted_tokens = [token for token in predicted_tokens if token not in [START_TOKEN, END_TOKEN, PADDING_TOKEN]]

# Convert predicted tokens to a readable sentence
predicted_sentence = " ".join(predicted_tokens)
print("Predicted sentence:", predicted_sentence)
```

In this code, I've added a check to stop generating if the sequence length exceeds the `max_sequence_length` of the model. This should resolve the error you were encountering.