code: def __init__(
      self,
      config: "OnnxConfig",
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Optional[Dict[str, Any]] = None,
  ):
      self._model = model
  
      patching_specs = config.PATCHING_SPECS
      self._patching_specs = []
      for spec in patching_specs if patching_specs is not None else []:
          final_spec = spec
          if spec.orig_op is None:
              final_spec = dataclasses.replace(
                  spec, orig_op=getattr(spec.o, spec.name)
              )
          self._patching_specs.append(final_spec)
  
      self.orig_forward_name = (
          "forward" if hasattr(self._model, "forward") else "call"
      )
      self.orig_forward = getattr(self._model, self.orig_forward_name)
  
      self.model_kwargs = (
          model_kwargs if model_kwargs is not None else {}
      )
  
      # TODO: remove that once we got rid of OnnxConfigWithLoss or we implemented it better.
      if config.__class__.__name__ == "OnnxConfigWithLoss":
          self.real_config = config._onnx_config
      else:
          self.real_config = config
  
      allow_past_in_outputs = (
          hasattr(self.real_config, "use_past")
          and self.real_config.use_past
      )
  
      @functools.wraps(self.orig_forward)
      def patched_forward(*args, **kwargs):
          signature = inspect.signature(self.orig_forward)
          args, kwargs = override_arguments(
              args, kwargs, signature, model_kwargs=self.model_kwargs
          )
  
          outputs = self.orig_forward(*args, **kwargs)
  
          # This code block handles different cases of the filterd_outputs input to align it with the expected
          # format of outputs. It is common for the output type of a model to vary, such as tensor, list,
          # tuple, etc. For Transformers models, the output is encapsulated in a ModelOutput object that
          # contains the output names of the model. In the case of Timm classification models, the output
          # is of type tensor. By default, it is assumed that the output names mentioned in the ONNX config
          # match the outputs in order.
          filterd_outputs = {}
          if isinstance(outputs, dict):
              for name, value in outputs.items():
                  onnx_output_name = (
                      config.torch_to_onnx_output_map.get(name, name)
                  )
                  if (
                      onnx_output_name in config.outputs
                      or (
                          allow_past_in_outputs
                          and name.startswith("past_key_values")
                      )
                      or any(
                          key.startswith(onnx_output_name)
                          for key in config.outputs.keys()
                      )
                  ):
                      filterd_outputs[name] = value
          elif isinstance(outputs, (list, tuple)):
              outputs_list = list(config.outputs.keys())
              dict(zip(outputs_list, outputs))
          else:
              if len(config.outputs) > 1:
                  num_outputs = len(config.outputs)
                  outputs_str = ", ".join(config.outputs.keys())
                  raise ValueError(
                      f"config.outputs should have only one outputs, but it has {num_outputs} keys: {outputs_str}"
                  )
              else:
                  name = list(config.outputs.keys())[0]
                  filterd_outputs[name] = outputs
              name = list(config.outputs.keys())[0]
              filterd_outputs[name] = outputs
          return filterd_outputs
  
      self.patched_forward = patched_forward
  
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 ModelPatcher object  with example: obj = ModelPatcher(config, model, model_kwargs)