code: def make_transformer(
      nlp: Language,
      name: str,
      model: Model[List[Doc], FullTransformerBatch],
      set_extra_annotations: Callable[
          [List[Doc], FullTransformerBatch], None
      ],
      max_batch_items: int,
  ):
      """Construct a Transformer component, which lets you plug a model from the
      Huggingface transformers library into spaCy so you can use it in your
      pipeline. One or more subsequent spaCy components can use the transformer
      outputs as features in its model, with gradients backpropagated to the single
      shared weights.
  
      model (Model[List[Doc], FullTransformerBatch]): A thinc Model object wrapping
          the transformer. Usually you will want to use the TransformerModel
          layer for this.
      set_extra_annotations (Callable[[List[Doc], FullTransformerBatch], None]): A
          callback to set additional information onto the batch of `Doc` objects.
          The doc._.trf_data attribute is set prior to calling the callback.
          By default, no additional annotations are set.
      """
      return Transformer(
          nlp.vocab,
          model,
          set_extra_annotations,
          max_batch_items=max_batch_items,
          name=name,
      )
  
imports: from typing import List, Callable, Iterable, Iterator, Optional, Dict, Union
  import warnings
  from spacy.language import Language
  from spacy.pipeline.trainable_pipe import TrainablePipe
  from spacy.pipeline.pipe import deserialize_config
  from spacy.tokens import Doc
  from spacy.vocab import Vocab
  from spacy.training import Example, validate_examples
  from spacy import util, Errors
  from spacy.util import minibatch
  from thinc.api import Model, Config, set_dropout_rate, Optimizer
  import srsly
  from pathlib import Path
  from .layers.transformer_model import huggingface_from_pretrained
  from .util import batch_by_length
  from .annotation_setters import null_annotation_setter
  from .data_classes import FullTransformerBatch, TransformerData
  from .layers import TransformerListener
label_desc: Use image node_id 1 for calling a global function with example usage: make_transformer(nlp, name, model, set_extra_annotations, max_batch_items) and returns: Transformer