code: def __init__(
      self,
      config: "OnnxConfig",
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Optional[Dict[str, Any]] = None,
  ):
      super().__init__(config, model, model_kwargs)
  
      if _transformers_version >= version.parse("4.36"):
          self.original_prepare_4d_causal_attention_mask_for_sdpa = (
              _prepare_4d_causal_attention_mask_for_sdpa
          )
          self.original_unmask_unattended = (
              AttentionMaskConverter._unmask_unattended
          )
  
      # TODO: Remove this if once transformers if much above 4.35
      if AttentionMaskConverter is not None:
          self.original_make_causal = (
              AttentionMaskConverter._make_causal_mask
          )
  
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 3 to create a new DecoderModelPatcher object from inherited base classes: ModelPatcher with example: obj = DecoderModelPatcher(config, model, model_kwargs)