code: def huggingface_tokenize(
      tokenizer, texts: List[str]
  ) -> BatchEncoding:
      """Apply a Huggingface tokenizer to a batch of texts."""
  
      # Use NumPy arrays rather than PyTorch tensors to avoid a lot of
      # host <-> device transfers during tokenization and post-processing
      # when a GPU is used.
      warnings.warn(
          "spacy_transformers.util.huggingface_tokenize has been moved to "
          "spacy_transformers.layers.transformer_model.huggingface_tokenize.",
          DeprecationWarning,
      )
      token_data = tokenizer(
          texts,
          add_special_tokens=True,
          return_attention_mask=True,
          return_offsets_mapping=isinstance(
              tokenizer, PreTrainedTokenizerFast
          ),
          return_tensors="np",
          return_token_type_ids=None,  # Sets to model default
          padding="longest",
      )
      token_data["input_texts"] = []
      for i in range(len(token_data["input_ids"])):
          wp_texts = tokenizer.convert_ids_to_tokens(
              token_data["input_ids"][i]
          )
          token_data["input_texts"].append(wp_texts)
      token_data["pad_token"] = tokenizer.pad_token
      return token_data
  
imports: from typing import List, Dict, Union, Set
  from pathlib import Path
  import random
  from transformers import AutoModel, AutoTokenizer
  from transformers.tokenization_utils import BatchEncoding
  from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
  import catalogue
  from spacy.util import registry
  from thinc.api import get_torch_default_device
  import torch.cuda
  import tempfile
  import shutil
  import contextlib
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: huggingface_tokenize(tokenizer, texts) and returns: token_data