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 from_onnx(args):
      d = OnnxAttributes()
      for arg in args:
          d[arg.name] = convertAttributeProto(arg)
      return d
  
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 1 for calling the OnnxAttributes obj's underlying member method code with example usage: obj.from_onnx(args) and returns: d