code: def rename_var(ckpt_path, new_ckpt_path, num_classes=5):
      with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as sess:
          var_list = tf.train.list_variables(ckpt_path)
          new_var_list = []
  
          for var_name, shape in var_list:
              # print(var_name)
              if var_name in except_list:
                  continue
  
              var = tf.train.load_variable(ckpt_path, var_name)
              new_var_name = var_name.replace("vgg_16", "feature")
              new_var_name = new_var_name.replace("weights", "kernel")
              new_var_name = new_var_name.replace("biases", "bias")
  
              new_var_name = new_var_name.replace(
                  "conv1/conv1_1", "conv2d"
              )
              new_var_name = new_var_name.replace(
                  "conv1/conv1_2", "conv2d_1"
              )
  
              new_var_name = new_var_name.replace(
                  "conv2/conv2_1", "conv2d_2"
              )
              new_var_name = new_var_name.replace(
                  "conv2/conv2_2", "conv2d_3"
              )
  
              new_var_name = new_var_name.replace(
                  "conv3/conv3_1", "conv2d_4"
              )
              new_var_name = new_var_name.replace(
                  "conv3/conv3_2", "conv2d_5"
              )
              new_var_name = new_var_name.replace(
                  "conv3/conv3_3", "conv2d_6"
              )
  
              new_var_name = new_var_name.replace(
                  "conv4/conv4_1", "conv2d_7"
              )
              new_var_name = new_var_name.replace(
                  "conv4/conv4_2", "conv2d_8"
              )
              new_var_name = new_var_name.replace(
                  "conv4/conv4_3", "conv2d_9"
              )
  
              new_var_name = new_var_name.replace(
                  "conv5/conv5_1", "conv2d_10"
              )
              new_var_name = new_var_name.replace(
                  "conv5/conv5_2", "conv2d_11"
              )
              new_var_name = new_var_name.replace(
                  "conv5/conv5_3", "conv2d_12"
              )
  
              if "fc" in new_var_name:
                  # new_var_name = new_var_name.replace("feature/fc6", "dense")
                  # new_var_name = new_var_name.replace("feature/fc7", "dense_1")
                  # new_var_name = new_var_name.replace("fc8", "dense_2")
                  continue
  
              #     print(new_var_name)
              re_var = tf.Variable(var, name=new_var_name)
              new_var_list.append(re_var)
  
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([25088, 2048]),
              name="dense/kernel",
          )
          new_var_list.append(re_var)
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([2048]),
              name="dense/bias",
          )
          new_var_list.append(re_var)
  
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([2048, 2048]),
              name="dense_1/kernel",
          )
          new_var_list.append(re_var)
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([2048]),
              name="dense_1/bias",
          )
          new_var_list.append(re_var)
  
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([2048, num_classes]),
              name="dense_2/kernel",
          )
          new_var_list.append(re_var)
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([num_classes]),
              name="dense_2/bias",
          )
          new_var_list.append(re_var)
  
          saver = tf.compat.v1.train.Saver(new_var_list)
          sess.run(tf.compat.v1.global_variables_initializer())
          saver.save(
              sess,
              save_path=new_ckpt_path,
              write_meta_graph=False,
              write_state=False,
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: rename_var(ckpt_path, new_ckpt_path, num_classes) without return types