code: def add_feats_numericalize_ops(c2_prepared, vocab_map, input_names):
      predict_net = (
          c2_prepared.predict_net
      )  # Protobuf of the predict_net
      (
          final_predict_net,
          init_net,
          final_input_names,
      ) = get_numericalize_net(c2_prepared, vocab_map, input_names)
      with c2_prepared.workspace._ctx:
          # Copy over the other list of the ops
          final_predict_net.Proto().op.extend(predict_net.op)
          # Update predict_net and init_net
          c2_prepared.predict_net = final_predict_net.Proto()
          c2_prepared.init_net = init_net.Proto()
  
      return c2_prepared, final_input_names
  
file_path: pytext/pytext/utils/onnx.py
imports: import caffe2.python.predictor.predictor_exporter
  import numpy
  import torch
  from caffe2.python import core, workspace
  from caffe2.python.onnx import backend
  from torch.onnx import ExportTypes, OperatorExportTypes
label_desc: Use image node_id 6 for calling a global function with example usage: add_feats_numericalize_ops(c2_prepared, vocab_map, input_names) and returns: c2_prepared,  final_input_names