code: def generate_in_out_map(ops, tensor_map, model_names_set):
      in_out_map = {}
      for op in ops:
          op.name = generate_obfuscated_name(
              "op", op.name, model_names_set
          )
          for input_name in op.input:
              if input_name not in in_out_map:
                  if input_name in tensor_map:
                      in_out_map[input_name] = tensor_map[input_name]
                  else:
                      in_out_map[input_name] = generate_obfuscated_name(
                          "in", input_name, model_names_set
                      )
          for output_name in op.output:
              if output_name not in in_out_map:
                  if output_name in tensor_map:
                      in_out_map[output_name] = tensor_map[output_name]
                  else:
                      in_out_map[
                          output_name
                      ] = generate_obfuscated_name(
                          "out", output_name, model_names_set
                      )
      return in_out_map
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import datetime
  import os
  import hashlib
  from jinja2 import Environment, FileSystemLoader
  from py_proto import mace_pb2
  from utils import device
  from utils import util
  from utils.util import mace_check
  from utils.util import MaceLogger
  from utils import config_parser
  from utils.config_parser import CPP_KEYWORDS
  from transform.base_converter import MaceKeyword
label_desc: Use image node_id 3 for calling a global function with example usage: generate_in_out_map(ops, tensor_map, model_names_set) and returns: in_out_map