e, sequence_length)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, RobertaForCausalLM, RobertaConfig
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
        >>> config = RobertaConfig.from_pretrained("roberta-base")
        >>> config.is_decoder = True
        >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config)
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> prediction_logits = outputs.logits
        ```
        NF)