code: def _onnx_node_to_caffe2_op(
      cls, init_model, pred_model, node_def, opset_version
  ):
      cbackend = C.Caffe2Backend(cls._dummy_name)
      if cbackend.support_onnx_import(node_def.op_type):
          # extract value infos from pred model (value infos of
          # node's inputs that are in init model should be all
          # available in pred model)
          value_infos = []
          for name in node_def.input:
              if pred_model is not None:
                  for vi in itertools.chain(
                      pred_model.graph.input,
                      pred_model.graph.output,
                      pred_model.graph.value_info,
                  ):
                      if vi.name == name:
                          value_infos.append(vi.SerializeToString())
  
          op_strs = cbackend.convert_node(
              node_def.SerializeToString(), value_infos, opset_version
          )
          init_ops = []
          for s in op_strs[0]:
              op = caffe2_pb2.OperatorDef()
              op.ParseFromString(s)
              init_ops.append(op)
          ops = []
          for s in op_strs[1]:
              op = caffe2_pb2.OperatorDef()
              op.ParseFromString(s)
              ops.append(op)
          return Caffe2Ops(ops, init_ops, [])
  
      if node_def.op_type in cls._special_operators:
          translator = getattr(
              cls, cls._special_operators[node_def.op_type]
          )
      else:
          translator = cls._common_onnx_node_to_caffe2_op
      ops = translator(
          init_model, pred_model, OnnxNode(node_def), opset_version
      )
      if isinstance(ops, Caffe2Ops):
          return ops
      if not isinstance(ops, collections.abc.Iterable):
          ops = [ops]
      return Caffe2Ops(ops, [], [])
  
imports: import collections
  import sys
  import zipfile
  import itertools
  import onnx.backend
  from caffe2.python import core, workspace, rnn_cell, gru_cell
  from caffe2.python.model_helper import ModelHelper
  from caffe2.proto import caffe2_pb2
  import caffe2.python.utils
  import numpy
  import onnx
  from onnx import TensorProto
  import onnx.numpy_helper
  import onnx.defs
  import onnx.shape_inference
  import onnx.utils
  from onnx.backend.base import Backend, Device, DeviceType, namedtupledict
  from caffe2.python.onnx.workspace import Workspace
  from caffe2.python.onnx.backend_rep import Caffe2Rep
  import caffe2.python._import_c_extension
  import warnings
label_desc: Use image node_id 19 for calling the Caffe2Backend obj's underlying member method code with example usage: obj._onnx_node_to_caffe2_op(cls, init_model, pred_model, node_def, opset_version) and returns: Caffe2Ops, Caffe2Ops, ops