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.
      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, Tuple, Callable, Union, Dict
  import copy
  from pathlib import Path
  from transformers.file_utils import ModelOutput
  from transformers import AutoConfig, AutoModel, AutoTokenizer
  from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
  from transformers.tokenization_utils import BatchEncoding
  from spacy.tokens import Doc
  from thinc.api import Model, get_torch_default_device, xp2torch
  from thinc.types import ArgsKwargs
  import logging
  from ..data_classes import FullTransformerBatch, WordpieceBatch, HFObjects
  from ..util import maybe_flush_pytorch_cache
  from ..util import log_gpu_memory, log_batch_size
  from ..layers._util import replace_listener, replace_listener_cfg
  from ..truncate import truncate_oversize_splits
  from ..align import get_alignment, get_alignment_via_offset_mapping
  from .hf_wrapper import HFWrapper
label_desc: Use image node_id 8 for calling a global function with example usage: huggingface_tokenize(tokenizer, texts) and returns: token_data