code: def _create_loop(cls, init_model, pred_model, n, opset_version):
      ops = cls._create_control_op(
          init_model, pred_model, n, opset_version
      )
      assert ops[0][0].type == "ONNXWhile"
      while_op = ops[0][0]
      while_op.arg.extend(
          [caffe2.python.utils.MakeArgument("has_trip_count", True)]
      )
      while_op.arg.extend(
          [caffe2.python.utils.MakeArgument("has_cond", True)]
      )
      while_op.arg.extend(
          [caffe2.python.utils.MakeArgument("disable_scopes", True)]
      )
      control_inputs = []
      for arg in while_op.arg:
          if arg.name == "__control_inputs":
              control_inputs = arg.strings
      num_loop_carried_deps = 0
      for arg in while_op.arg:
          if arg.name == "body":
              num_loop_carried_deps = len(arg.n.external_input) - 2
              arg.n.external_input.extend(control_inputs)
      while_op.arg.extend(
          [
              caffe2.python.utils.MakeArgument(
                  "num_loop_carried_deps", num_loop_carried_deps
              )
          ]
      )
  
      return 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 10 for calling the Caffe2Backend obj's underlying member method code with example usage: obj._create_loop(cls, init_model, pred_model, n, opset_version) and returns: ops