def METHOD_NAME(self, texts: List[str]):
    tokenized_texts = [self.tokenizer.METHOD_NAME(text) for text in texts]
    sentence_lengths = [len(tokens) for tokens in tokenized_texts]
    max_len = max(sentence_lengths)
    input_ids = []
    attention_masks = []
    for tokens in tokenized_texts:
        padding = [0] * (max_len - len(tokens))
        input_ids.append(tokens + padding)
        attention_masks.append([1]*len(tokens) + padding)
    output = {'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_masks, dtype=torch.long),
            'sentence_lengths': torch.tensor(sentence_lengths, dtype=torch.long)}
    return output