code: def Transformer(name: str) -> Model[TokensPlus, List[Floats2d]]:
      return PyTorchWrapper(
          AutoModel.from_pretrained(name),
          convert_inputs=convert_transformer_inputs,
          convert_outputs=convert_transformer_outputs,
      )
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Tuple, Callable
  import torch
  from pathlib import Path
  from transformers import AutoTokenizer, AutoModel
  import thinc
  from thinc.api import PyTorchWrapper, Softmax, chain, with_array, Model, Config
  from thinc.api import torch2xp, xp2torch, SequenceCategoricalCrossentropy
  from thinc.api import prefer_gpu, use_pytorch_for_gpu_memory
  from thinc.types import Floats2d, ArgsKwargs
  import ml_datasets
  import tqdm
  import typer
label_desc: Use image node_id 4 for calling a global function with example usage: Transformer(name) and returns: PyTorchWrapper