code: def tokenize(
      texts: Union[str, List[str]],
      tokenizer: Any,
      context_length: int,
      add_extra_token: int,
  ) -> torch.LongTensor:
      """
      Returns the tokenized representation of given input string(s). If the list of tokens exceeds the context
      length plus the number of extra tokens, it gets truncated. If it's smaller, it gets padded with zeros.
  
      Parameters
      ----------
      texts : Union[str, List[str]]
          An input string or a list of input strings to tokenize.
      tokenizer : Any
          A tokenizer to be used for tokenization.
      context_length : int
          The context length to be used for the output tensor.
      add_extra_token : int
          Number of extra tokens to add, should be either 0 or 1.
  
      Returns
      -------
      torch.LongTensor
          A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length + add_extra_token].
      """
      assert (
          add_extra_token == 0 or add_extra_token == 1
      ), "`add_extra_token` should be either 0 or 1."
  
      texts_is_str = False
      if isinstance(texts, str):
          texts = [texts]
          texts_is_str = True
      tokens = tokenizer.text_to_ids(texts)
      max_len = max([len(token) for token in tokens])
      context_length = min(max_len - add_extra_token, context_length)
      # truncate and padding
      result = torch.zeros(
          len(tokens),
          context_length + add_extra_token,
          dtype=torch.long,
      )
  
      for i, token in enumerate(tokens):
          if len(token) > context_length + add_extra_token:
              token = token[
                  : context_length + add_extra_token
              ]  # Truncate
          result[i, : len(token)] = torch.tensor(token)
      if texts_is_str:
          result = result[0]
      return result
  
imports: import copy
  import json
  import logging
  import os
  import re
  import tarfile
  from dataclasses import dataclass
  from typing import Any, Dict, List, Sequence, Union
  import torch
  import torch.nn.functional
  import transformers
  from einops import rearrange
  from omegaconf import DictConfig
  from PIL import Image
  from torch.utils.data import Dataset, default_collate
  from transformers import CLIPImageProcessor
  import nemo.collections.multimodal.data.neva.conversation
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
label_desc: Use image node_id 1 for calling a global function with example usage: tokenize(texts, tokenizer, context_length, add_extra_token) and returns: result