code: def caffe2_op_to_onnx_node(cls, op_def, shapes):
      if C.support_onnx_export(op_def.type):
          node_strs, tensor_strs = C.export_to_onnx(
              cls._dummy_name, op_def.SerializeToString(), shapes
          )
          nodes = []
          for s in node_strs:
              node = NodeProto()
              node.ParseFromString(s)
              nodes.append(node)
          const_tensors = []
          for s in tensor_strs:
              tensor = TensorProto()
              tensor.ParseFromString(s)
              const_tensors.append(tensor)
          return nodes, const_tensors
      elif op_def.type in cls._special_operators:
          translator = getattr(cls, cls._special_operators[op_def.type])
      else:
          translator = cls._common_caffe2_op_to_onnx_node
      nodes = translator(op_def, shapes)
      const_tensors = []
      if isinstance(nodes, tuple):
          nodes, const_tensors = nodes
      if not isinstance(nodes, collections.abc.Iterable):
          nodes = [nodes]
      return nodes, const_tensors
  
imports: import collections
  import itertools
  import logging
  import re
  from caffe2.python import core
  from onnx import checker, helper, numpy_helper, mapping, GraphProto, NodeProto, TensorProto, OperatorSetIdProto
  from onnx.helper import make_tensor_value_info, make_model
  import numpy
  from caffe2.python.onnx.helper import c2_native_run_net
  import caffe2.python._import_c_extension
label_desc: Use image node_id 5 for calling the Caffe2Frontend obj's underlying member method code with example usage: obj.caffe2_op_to_onnx_node(cls, op_def, shapes) and returns: nodes,  const_tensors, nodes,  const_tensors