le_english_phrase = "42 is the answer"
    >>> inputs = tokenizer(example_english_phrase, return_tensors="tf")

    >>> # Translate
    >>> generated_ids = model.generate(**inputs, num_beams=4, max_length=5)
    >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    '42 este răspuns'
    ```

    Mask filling example:

    ```python
    >>> from transformers import AutoTokenizer, TFMBartForConditionalGeneration
    >>> import tensorflow as tf

    >>> model = TFMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

    >>> # de_DE is the language symbol id <LID> for German
    >>> TXT = "</s> Meine Freunde sind <mask> nett aber sie essen zu viel Kuchen. </s> de_DE"

    >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="tf")["input_ids"]
    >>> logits = model(input_ids).logits

    >>> masked_index = tf.where(input_ids[0] == tokenizer.mask_token_id)[0, 0]
    >>> probs = tf.nn.softmax(logits[0, masked_index], axis=0)
    >>> values, predictions = tf.math.top_k(probs, 5)

    >>> tokenizer.decode(predictions).split()
    ['nett', 'sehr', 'ganz', 'nicht', 'so']
    ```
c