code: def init(model: TransformerModel, X=None, Y=None):
      if model.attrs["has_transformer"]:
          return
      name = model.attrs["name"]
      tok_cfg = model._init_tokenizer_config
      trf_cfg = model._init_transformer_config
      hf_model = huggingface_from_pretrained(name, tok_cfg, trf_cfg)
      model.attrs["set_transformer"](model, hf_model)
      tokenizer = model.tokenizer
      # Call the model with a batch of inputs to infer the width
      if X:
          # If we're dealing with actual texts, do the work to setup the wordpieces
          # batch properly
          docs = X
          get_spans = model.attrs["get_spans"]
          nested_spans = get_spans(docs)
          flat_spans = []
          for doc_spans in nested_spans:
              flat_spans.extend(doc_spans)
          token_data = huggingface_tokenize(
              tokenizer, [span.text for span in flat_spans]
          )
          wordpieces = WordpieceBatch.from_batch_encoding(token_data)
          if "offset_mapping" in token_data:
              align = get_alignment_via_offset_mapping(
                  flat_spans,
                  token_data["offset_mapping"],
              )
          else:
              align = get_alignment(
                  flat_spans,
                  wordpieces.strings,
                  tokenizer.all_special_tokens,
              )
          wordpieces, align = truncate_oversize_splits(
              wordpieces, align, tokenizer.model_max_length
          )
      else:
          texts = ["hello world", "foo bar"]
          token_data = huggingface_tokenize(tokenizer, texts)
          wordpieces = WordpieceBatch.from_batch_encoding(token_data)
      model.layers[0].initialize(X=wordpieces)
      model_output = model.layers[0].predict(wordpieces)
      model.set_dim("nO", model_output.last_hidden_state.shape[-1])
  
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 3 for calling a global function with example usage: init(model, X, Y) without return types