code: def execute(self, save_path, model_params):
      onnx_path = save_path / f"{self.model_name}{self.ONNX_EXTENSION}"
      try:
          model_onnx = onnx.load(str(self.model))
          onnx.save(model_onnx, str(onnx_path))
      except Exception:
          self.logger.error(
              "The provided onnx model path is invalid. Please provide"
              " a valid path to a model in order to use Nebullvm."
          )
          self.converted_models = []
  
      self.converted_models = [str(onnx_path)]
  
imports: import abc
  from pathlib import Path
  from typing import Optional, List, Union
  from nebullvm.core.models import DeviceType, DeepLearningFramework, ModelParams
  from nebullvm.operations.base import Operation
  from nebullvm.operations.conversions.pytorch import convert_torch_to_onnx
  from nebullvm.operations.conversions.tensorflow import convert_tf_to_onnx
  from nebullvm.optional_modules.onnx import onnx
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.data import DataManager
label_desc: Use image node_id 1 for calling the ONNXConverter obj's underlying member method code with example usage: obj.execute(save_path, model_params) without return types