[DOCS] add example NoBadWordsLogitsProcessor (#25046)

* add example NoBadWordsLogitsProcessor

* fix L764 & L767

* make style

src/transformers/generation/logits_process.py

@@ -747,6 +747,50 @@ class NoBadWordsLogitsProcessor(SequenceBiasLogitsProcessor):
             List of list of token ids that are not allowed to be generated.
         eos_token_id (`Union[int, List[int]]`):
             The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> inputs = tokenizer(["In a word, the cake is a"], return_tensors="pt")
+
+    >>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=5, pad_token_id=tokenizer.eos_token_id)
+    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
+    In a word, the cake is a bit of a mess.
+
+    >>> # Now let's control generation taking the bad words out. Please note that the tokenizer is initialized differently
+
+    >>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("gpt2", add_prefix_space=True)
+
+
+    >>> def get_tokens_as_list(word_list):
+    ...     "Converts a sequence of words into a list of tokens"
+    ...     tokens_list = []
+    ...     for word in word_list.split(" "):
+    ...         tokenized_word = tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0]
+    ...         tokens_list.append(tokenized_word)
+    ...     return tokens_list
+
+
+    >>> word_list = "mess"
+    >>> bad_words_ids = get_tokens_as_list(word_list=word_list)
+
+    >>> badwords_ids = model.generate(
+    ...     inputs["input_ids"],
+    ...     max_new_tokens=5,
+    ...     bad_words_ids=bad_words_ids,
+    ...     eos_token_id=tokenizer_with_prefix_space.eos_token_id,
+    ... )
+    >>> print(tokenizer.batch_decode(badwords_ids, skip_special_tokens=True)[0])
+    In a word, the cake is a bit of a surprise.
+
+    >>> badwords_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=5, bad_words_ids=bad_words_ids)
+    >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
+    In a word, the cake is a great way to start
+    ```
     """
 
     def __init__(self, bad_words_ids: List[List[int]], eos_token_id: Union[int, List[int]]):