h_size, sequence_length)`, *optional*):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
            ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
        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, RemBertForCausalLM, RemBertConfig
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
        >>> config = RemBertConfig.from_pretrained("google/rembert")
        >>> config.is_decoder = True
        >>> model = RemBertForCausalLM.from_pretrained("google/rembert", config=config)

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> prediction_logits = outputs.logits
        ```N)