code: def rename_var(ckpt_path, m_info):
      with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as sess:
          var_list = tf.train.list_variables(ckpt_path)
          pattern = "ExponentialMovingAverage|Momentum|global_step"
  
          var_dict = dict(
              (change_word(name), [name, shape])
              for name, shape in var_list
              if len(re.findall(pattern, name)) == 0
          )
  
          for k, v in m_info:
              assert k in var_dict, "{} not in var_dict".format(k)
              assert (
                  v == var_dict[k][1]
              ), "shape {} not equal {}".format(v, var_dict[k][1])
  
          weights = []
          for k, _ in m_info:
              var = tf.train.load_variable(ckpt_path, var_dict[k][0])
              weights.append(var)
  
          return weights
  
imports: import re
  import tensorflow
  from model_v3 import mobilenet_v3_large
label_desc: Use image node_id 2 for calling a global function with example usage: rename_var(ckpt_path, m_info) and returns: weights