pyton
    >>> from transformers import AutoTokenizer, FlaxPegasusForConditionalGeneration

    >>> model = FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
    >>> tokenizer = AutoTokenizer.from_pretrained('google/pegasus-large')

    >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np')

    >>> # Generate Summary
    >>> summary_ids = model.generate(inputs['input_ids']).sequences
    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
    ```

    Mask filling example:

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

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

    >>> model = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-large")
    >>> input_ids = tokenizer([TXT], return_tensors="np")["input_ids"]
    >>> logits = model(input_ids).logits

    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
    >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
    >>> values, predictions = jax.lax.top_k(probs)

    >>> tokenizer.decode(predictions).split()
    ```
r