ab_size]`

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, XLMProphetNetForCausalLM
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
        >>> model = XLMProphetNetForCausalLM.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits

        >>> # Model can also be used with EncoderDecoder framework
        >>> from transformers import BertTokenizer, EncoderDecoderModel, AutoTokenizer
        >>> import torch

        >>> tokenizer_enc = BertTokenizer.from_pretrained("bert-large-uncased")
        >>> tokenizer_dec = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained(
        ...     "bert-large-uncased", "patrickvonplaten/xprophetnet-large-uncased-standalone"
        ... )

        >>> ARTICLE = (
        ...     "the us state department said wednesday it had received no "
        ...     "formal word from bolivia that it was expelling the us ambassador there "
        ...     "but said the charges made against him are `` baseless ."
        ... )
        >>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
        >>> labels = tokenizer_dec(
        ...     "us rejects charges against its ambassador in bolivia", return_tensors="pt"
        ... ).input_ids
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:])

        >>> loss = outputs.loss
        ```N)