code: def rename_var(pth_path, new_ckpt_path, num_classes):
      pytorch_dict = torch.load(pth_path)
  
      with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as sess:
          new_var_list = []
  
          for key, value in pytorch_dict.items():
              if key in except_list:
                  continue
  
              new_name = key
              value = value.detach().numpy()
  
              if "features.0" in new_name:
                  new_name = new_name.replace(
                      "features.0.weight", "conv2d/kernel"
                  )
                  new_name = new_name.replace(
                      "features.0.bias", "conv2d/bias"
                  )
  
              if "features.3" in new_name:
                  new_name = new_name.replace(
                      "features.3.weight", "conv2d_1/kernel"
                  )
                  new_name = new_name.replace(
                      "features.3.bias", "conv2d_1/bias"
                  )
  
              if "features.6" in new_name:
                  new_name = new_name.replace(
                      "features.6.weight", "conv2d_2/kernel"
                  )
                  new_name = new_name.replace(
                      "features.6.bias", "conv2d_2/bias"
                  )
  
              if "features.8" in new_name:
                  new_name = new_name.replace(
                      "features.8.weight", "conv2d_3/kernel"
                  )
                  new_name = new_name.replace(
                      "features.8.bias", "conv2d_3/bias"
                  )
  
              if "features.10" in new_name:
                  new_name = new_name.replace(
                      "features.10.weight", "conv2d_4/kernel"
                  )
                  new_name = new_name.replace(
                      "features.10.bias", "conv2d_4/bias"
                  )
  
              if "classifier.1" in new_name:
                  new_name = new_name.replace(
                      "classifier.1.weight", "dense/kernel"
                  )
                  new_name = new_name.replace(
                      "classifier.1.bias", "dense/bias"
                  )
  
              if "classifier.4" in new_name:
                  new_name = new_name.replace(
                      "classifier.4.weight", "dense_1/kernel"
                  )
                  new_name = new_name.replace(
                      "classifier.4.bias", "dense_1/bias"
                  )
  
              if "conv2d" in new_name and "kernel" in new_name:
                  value = np.transpose(value, (2, 3, 1, 0)).astype(
                      np.float32
                  )
              else:
                  value = np.transpose(value).astype(np.float32)
  
              re_var = tf.Variable(value, name=new_name)
              new_var_list.append(re_var)
  
          re_var = tf.Variable(
              tf.keras.initializers.he_uniform()([4096, 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(pth_path, new_ckpt_path, num_classes) without return types