code: def _rnn_reform_weights(
      cls, reforms, name, hidden_size, init_net, gates, reorder_indices
  ):
      for name_from, name_to, do_concat, extra_dims in reforms:
          gate_blobs = [
              "%s/%s_%s" % (name, prefix, name_to) for prefix in gates
          ]
          for i, x in enumerate(gate_blobs):
              dim0 = i * hidden_size, (i + 1) * hidden_size
              starts, ends = zip(dim0, *extra_dims)
              init_net.Slice(name_from, x, starts=starts, ends=ends)
          if do_concat:
              reordered_gate_blobs = [
                  gate_blobs[i] for i in reorder_indices
              ]
              init_net.Concat(
                  reordered_gate_blobs,
                  ["%s/%s" % (name, name_to), cls.dummy_name()],
                  axis=0,
              )
  
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 4 for calling the Caffe2Backend obj's underlying member method code with example usage: obj._rnn_reform_weights(cls, reforms, name, hidden_size, init_net, gates, reorder_indices) without return types