Here is an example of how to use the loaded BERT model to predict a masked word:

```python
# First, you need to prepare the input text and convert it to a tensor
text = "[CLS] This is a sentence with a masked [MASK] word."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)

# Move the input tensor to the GPU if available
if torch.cuda.is_available():
  input_ids = input_ids.cuda()

# Predict the masked word
with torch.no_grad():
  outputs = model(input_ids)
  predicted_token = torch.argmax(outputs[0][0, input_ids[0].tolist().index(103)])
  predicted_word = tokenizer.decode([predicted_token.item()])

# Print the predicted word
print(f"Predicted word: {predicted_word}")```

In this example, the input text is first tokenized using the BERT tokenizer, and then converted to a tensor. The masked word is identified by the `[MASK]` token, which has an encoding of 103. The BERT model is then used to predict the masked word, and the predicted word is decoded using the BERT tokenizer and printed.
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