code: def tokenize(self, texts: List[str]):
      tokenized_texts = [
          self.tokenizer.tokenize(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
  
imports: import torch
  from torch import nn
  from typing import List
  import logging
  import gzip
  from tqdm import tqdm
  import numpy
  import os
  import json
  from ..util import import_from_string, fullname, http_get
  from .tokenizer import WordTokenizer, WhitespaceTokenizer
label_desc: Use image node_id 3 for calling the WordEmbeddings obj's underlying member method code with example usage: obj.tokenize(texts) and returns: output