code: def _load_tokenizer_and_model(
      model_name_or_path: Union[str, os.PathLike],
      device: Optional[Union[str, torch.device]] = None,
  ) -> Tuple["PreTrainedTokenizerBase", "PreTrainedModel"]:
      """Load HuggingFace `transformers`' tokenizer and model. This function also handle a device placement.
  
      Args:
          model_name_or_path:
              A name or a model path used to load `transformers` pretrained model.
          device:
              A device to be used for calculation.
  
      Return:
          Initialized `transformers`' tokenizer and model.
  
      """
      from transformers import AutoModelForMaskedLM, AutoTokenizer
  
      tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
      model = AutoModelForMaskedLM.from_pretrained(model_name_or_path)
      model.eval()
      model.to(device)
      return tokenizer, model
  
imports: import math
  import os
  from collections import Counter, defaultdict
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Union
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset
  from torchmetrics.utilities.data import _cumsum
  from torchmetrics.utilities.imports import _TQDM_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 8 for calling a global function with example usage: _load_tokenizer_and_model(model_name_or_path, device) and returns: tokenizer,  model