ns=False)
        ... ).unsqueeze(
        ...     0
        ... )  # We will predict the masked token
        >>> labels = torch.tensor(tokenizer.encode("cute", add_special_tokens=False)).unsqueeze(0)
        >>> assert labels.shape[0] == 1, "only one word will be predicted"
        >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
        >>> perm_mask[
        ...     :, :, -1
        ... ] = 1.0  # Previous tokens don't see last token as is done in standard auto-regressive lm training
        >>> target_mapping = torch.zeros(
        ...     (1, 1, input_ids.shape[1]), dtype=torch.float
        ... )  # Shape [1, 1, seq_length] => let's predict one token
        >>> target_mapping[
        ...     0, 0, -1
        ... ] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)

        >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping, labels=labels)
        >>> loss = outputs.loss
        >>> next_token_logits = (
        ...     outputs.logits
        ... )  # Logits have shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
        ```NŠ