# Generate Summary
    >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5)
    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
    ```

    Mask filling example:

    ```python
    >>> from transformers import AutoTokenizer, TFBartForConditionalGeneration

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
    >>> TXT = "My friends are <mask> but they eat too many carbs."

    >>> model = TFBartForConditionalGeneration.from_pretrained("facebook/bart-large")
    >>> input_ids = tokenizer([TXT], return_tensors="tf")["input_ids"]
    >>> logits = model(input_ids).logits
    >>> probs = tf.nn.softmax(logits[0])
    >>> # probs[5] is associated with the mask token
    ```
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