code: def _convert_transformer_outputs(model, inputs_outputs, is_train):
      _, model_output = inputs_outputs
  
      def backprop(d_model_output: ModelOutput) -> ArgsKwargs:
          return ArgsKwargs(
              args=(model_output.last_hidden_state,),
              kwargs={"grad_tensors": d_model_output.values()},
          )
  
      return model_output, backprop
  
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 6 for calling a global function with example usage: _convert_transformer_outputs(model, inputs_outputs, is_train) and returns: model_output,  backprop, ArgsKwargs