code: def optimize_onnx(input, init=False, predict=False):
      passes = [
          "fuse_consecutive_transposes",
          "eliminate_nop_transpose",
          "fuse_transpose_into_gemm",
          "lift_lexical_references",
      ]
      if init:
          passes.append("split_init")
      if predict:
          passes.append("split_predict")
      try:
          out = onnx.optimizer.optimize(input, passes)
      except AttributeError:
          warnings.warn(
              "OptimizerWarning: optimizer module not found in ONNX version {}".format(
                  onnx.__version__
              )
          )
          # ONNX does no ship onnx.optimizer since version 1.9+
          import onnxoptimizer
  
          out = onnxoptimizer.optimize(input, passes)
      return out
  
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 16 for calling the Caffe2Backend obj's underlying member method code with example usage: obj.optimize_onnx(input, init, predict) and returns: out