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, pixel_values):
          vision_outputs = model[0].model.vision_model(
              pixel_values=pixel_values
          )
          image_embeds = model[0].model.visual_projection(
              vision_outputs[1]
          )
  
          text_outputs = model[0].model.text_model(
              input_ids=input_ids,
              attention_mask=attention_mask,
          )
          text_embeds = model[0].model.text_projection(text_outputs[1])
  
          if len(model) > 1:
              image_embeds = model[1:](image_embeds)
              text_embeds = model[1:](text_embeds)
  
          return {
              "text_embeds": text_embeds,
              "image_embeds": image_embeds,
          }
  
      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 SentenceTransformersCLIPPatcher object from inherited base classes: ModelPatcher with example: obj = SentenceTransformersCLIPPatcher(config, model, model_kwargs)