code: def __init__(
      self,
      config: "OnnxConfig",
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      model_kwargs: Dict[str, Any],
  ):
      super().__init__(config, model, model_kwargs)
  
      def patched_forward(input_ids, attention_mask):
          result = self.orig_forward(
              {"input_ids": input_ids, "attention_mask": attention_mask}
          )
  
          if "input_ids" in result:
              del result["input_ids"]
          if "attention_mask" in result:
              del result["attention_mask"]
          if "all_layer_embeddings" in result:
              del result["all_layer_embeddings"]
  
          return result
  
      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 SentenceTransformersTransformerPatcher object from inherited base classes: ModelPatcher with example: obj = SentenceTransformersTransformerPatcher(config, model, model_kwargs)