code: def _get_special_tokens_map(
      tokenizer: "PreTrainedTokenizerBase",
  ) -> Dict[str, int]:
      """Build a dictionary of model/tokenizer special tokens.
  
      Args:
          tokenizer:
              Initialized tokenizer from HuggingFace's `transformers package.
  
      Return:
          A dictionary containing: mask_token_id, pad_token_id, sep_token_id and cls_token_id.
  
      """
      return {
          "mask_token_id": tokenizer.mask_token_id,
          "pad_token_id": tokenizer.pad_token_id,
          "sep_token_id": tokenizer.sep_token_id,
          "cls_token_id": tokenizer.cls_token_id,
      }
  
imports: import os
  from enum import unique
  from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from typing_extensions import Literal
  from torchmetrics.functional.text.helper_embedding_metric import TokenizedDataset, _get_progress_bar, _input_data_collator, _load_tokenizer_and_model
  from torchmetrics.utilities.enums import EnumStr
  from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 2 for calling a global function with example usage: _get_special_tokens_map(tokenizer) and returns: dict