class_docstr: Handles decoder-based text architectures.
class_docstr_tok: ["Handles","decoder-based","text","architectures","."]
code: def patch_model_for_export(
      self,
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Optional[Dict[str, Any]] = None,
  ) -> "ModelPatcher":
      # Refer to DecoderModelPatcher.
      return DecoderModelPatcher(self, model, model_kwargs=model_kwargs)
  
imports: from collections import OrderedDict
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
  from transformers.utils import is_tf_available
  from ...onnx import merge_decoders
  from ...utils import DummyAudioInputGenerator, DummyBboxInputGenerator, DummyInputGenerator, DummyPastKeyValuesGenerator, DummySeq2SeqDecoderTextInputGenerator, DummySeq2SeqPastKeyValuesGenerator, DummyTextInputGenerator, DummyVisionInputGenerator, is_diffusers_available, logging
  from .base import ConfigBehavior, OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
  from .constants import ONNX_DECODER_MERGED_NAME, ONNX_DECODER_NAME, ONNX_DECODER_WITH_PAST_NAME
  from .model_patcher import DecoderModelPatcher
label_desc: Use image node_id 5 for calling the TextDecoderOnnxConfig obj's underlying member method code with example usage: obj.patch_model_for_export(model, model_kwargs) and returns: DecoderModelPatcher