class_docstr: This is a more convenient way to work with ONNX/Caffe2 attributes
  that is not the protobuf representation.
class_docstr_tok: ["This","is","a","more","convenient","way","to","work","with","ONNX\/Caffe2","attributes","that","is","not","the","protobuf","representation","."]
code: def caffe2(self, kmap=lambda k: k):
      for k, v in self.items():
          if kmap(k) != "":
              yield caffe2.python.utils.MakeArgument(kmap(k), v)
  
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 2 for calling the OnnxAttributes obj's underlying member method code with example usage: obj.caffe2(kmap) without return types