code: def execute(
      self,
      save_path: Path,
      model_params: ModelParams,
  ):
      self.converted_models = [self.model]
  
      if self.device.type not in self.SUPPORTED_DEVICES:
          return
  
      for framework in self.DEST_FRAMEWORKS:
          if framework is DeepLearningFramework.NUMPY:
              self.onnx_conversion(save_path, model_params)
          else:
              raise NotImplementedError()
  
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 PytorchConverter obj's underlying member method code with example usage: obj.execute(save_path, model_params) without return types