code: def get_onnx_model(
      model_name, onnx_models_path=saved_models_path, quantized=True
  ):
      """
      method gets the onnx model, if already converted models exists
      Example:
      >> get_onnx_model(model_name="t5-finetuned", onnx_models_path="../models/onnx/quantized/")
  
      """
  
      encoder_path, decoder_path, init_decoder_path = get_model_paths(
          model_name, Path(onnx_models_path), quantized
      )
  
      if quantized:
          assert (
              encoder_path.exists()
              and decoder_path.exists()
              and init_decoder_path.exists()
          ), "quantized model don't exist in the model folder, first quantize the model!"
      else:
          assert (
              encoder_path.exists()
              and decoder_path.exists()
              and init_decoder_path.exists()
          ), "all or some models don't exists in the model folder, first convert the model! "
  
      model_paths = encoder_path, decoder_path, init_decoder_path
  
      model_sessions = get_onnx_runtime_sessions(model_paths)
  
      model = OnnxT5(model_name, model_sessions)
  
      return model
  
imports: from .huggingface_utils import get_auth_token
  from .ort_settings import get_onnx_runtime_sessions
  from .onnx_exporter import generate_onnx_representation, quantize, get_model_paths, saved_models_path
  from pathlib import Path
  from transformers import AutoConfig, MT5Config, T5ForConditionalGeneration
  from transformers.modeling_outputs import Seq2SeqLMOutput, BaseModelOutput
  import torch
  import functools
  import operator
label_desc: Use image node_id 2 for calling a global function with example usage: get_onnx_model(model_name, onnx_models_path, quantized) and returns: model