code: def tokenize(path: Path) -> Tuple[Tensor, Dictionary]:
      dictionary = Dictionary()
  
      assert os.path.exists(path)
      # Add words to the dictionary
      with open(path, encoding="utf8") as f:
          for line in f:
              words = line.split() + ["<eos>"]
              for word in words:
                  dictionary.add_word(word)
  
      # Tokenize file content
      with open(path, encoding="utf8") as f:
          idss: List[Tensor] = []
          for line in f:
              words = line.split() + ["<eos>"]
              ids: List[int] = []
              for word in words:
                  ids.append(dictionary.word2idx[word])
              idss.append(torch.tensor(ids).type(torch.int64))
  
      return torch.cat(idss), dictionary
  
imports: import math
  import os
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import requests
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.modules import MultiheadAttention
  from torch.utils.data import DataLoader, Dataset
  from lightning.pytorch import LightningModule
label_desc: Use image node_id 1 for calling a global function with example usage: tokenize(path) and returns: torch,  dictionary