SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:

            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

              - `nbest_size = {0,1}`: No sampling is performed.
              - `nbest_size > 1`: samples from the nbest_size results.
              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.

            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
              BPE-dropout.

    Examples:

    ```python
    >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

    >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
    >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
    >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
    >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
    >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
    >>> outputs = model(**model_inputs)  # should work
    ```Z	input_idsZattention_maskÚ
prefix_tokensÚ
suffix_tokensNú