_TO_SUMMARIZE = (
    ...     "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
    ...     "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
    ...     "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
    ... )
    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")

    >>> # Generate Summary
    >>> summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
    >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions'
    ```

    Mask filling example:

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

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
    >>> model = BartForConditionalGeneration.from_pretrained("facebook/bart-base")

    >>> TXT = "My friends are <mask> but they eat too many carbs."
    >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
    >>> logits = model(input_ids).logits

    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
    >>> probs = logits[0, masked_index].softmax(dim=0)
    >>> values, predictions = probs.topk(5)

    >>> tokenizer.decode(predictions).split()
    ['not', 'good', 'healthy', 'great', 'very']
    ```
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