Code about DataCollatorForWholeWordMask in github

Hi, guys, I am reading the code about doing whole word mask pretraining.
Here is some code pieces of function _whole_word_mask function from DataCollatorForWholeWordMask. transformers/data_collator.py at 3f936df66287f557c6528912a9a68d7850913b9b 路 huggingface/transformers 路 GitHub

def _whole_word_mask(self, input_tokens: List[str], max_predictions=512):
        """
        Get 0/1 labels for masked tokens with whole word mask proxy
        """
        if not isinstance(self.tokenizer, (BertTokenizer, BertTokenizerFast)):
            warnings.warn(
                "DataCollatorForWholeWordMask is only suitable for BertTokenizer-like tokenizers. "
                "Please refer to the documentation for more information."
            )

        cand_indexes = []
        for i, token in enumerate(input_tokens):
            if token == "[CLS]" or token == "[SEP]":
                continue

            if len(cand_indexes) >= 1 and token.startswith("##"):
                cand_indexes[-1].append(i)
            else:
                cand_indexes.append([i])

        random.shuffle(cand_indexes)
        num_to_predict = min(max_predictions, max(1, int(round(len(input_tokens) * self.mlm_probability))))
        masked_lms = []
        covered_indexes = set()
        for index_set in cand_indexes:
            if len(masked_lms) >= num_to_predict:
                break
            # If adding a whole-word mask would exceed the maximum number of
            # predictions, then just skip this candidate.
            if len(masked_lms) + len(index_set) > num_to_predict:
                continue
            is_any_index_covered = False
            for index in index_set:
                if index in covered_indexes:
                    is_any_index_covered = True
                    break
            if is_any_index_covered:
                continue
            for index in index_set:
                covered_indexes.add(index)
                masked_lms.append(index)

        if len(covered_indexes) != len(masked_lms):
            raise ValueError("Length of covered_indexes is not equal to length of masked_lms.")
        mask_labels = [1 if i in covered_indexes else 0 for i in range(len(input_tokens))]
        return mask_labels

where the input tokens will be something like [鈥榌CLS]鈥, 鈥榠鈥, 鈥榣ove鈥, 鈥榥鈥, 鈥##lp鈥, 鈥榌SEP]鈥橾 with a origin input
鈥渋 love nlp鈥 and the function will select the word to be masked randomly with the whole word mask principle.

My question how could the variable is_any_index_covered be set to True. With my understanding, there should not be any duplicate index in different sub_list of cand_indexes, so how could a index get covered more than one time, could someone give me an example? Thanks