code: def __init__(
      self,
      config: "OnnxConfig",
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Optional[Dict[str, Any]] = None,
  ):
      super().__init__(config, model, model_kwargs)
      use_cache = hasattr(self.real_config, "use_past")
  
      if (
          config._behavior == "decoder"
          and model.config.decoder.model_type == "trocr"
          and use_cache
      ):
          model.decoder.model.decoder.config.use_cache = True
  
imports: import dataclasses
  import functools
  import inspect
  import math
  import sys
  import types
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import transformers
  from packaging import version
  from transformers.models.speecht5.modeling_speecht5 import SpeechT5EncoderWithSpeechPrenet
  from transformers.utils import is_torch_available
  from ...configuration_utils import _transformers_version
  from ...utils import logging
label_desc: Use image node_id 1 to create a new VisionEncoderDecoderPatcher object from inherited base classes: Seq2SeqModelPatcher with example: obj = VisionEncoderDecoderPatcher(config, model, model_kwargs)