code: def test_gluon_trainer_type():
      def check_trainer_kv_update(weight_stype, update_on_kv):
          x = mx.gluon.Parameter(
              "x", shape=(10, 1), lr_mult=1.0, stype=weight_stype
          )
          x.initialize(ctx=[mx.cpu(0), mx.cpu(1)], init="zeros")
          try:
              trainer = mx.gluon.Trainer(
                  [x],
                  "sgd",
                  {"learning_rate": 0.1},
                  kvstore=kv,
                  update_on_kvstore=update_on_kv,
              )
              trainer._init_kvstore()
              assert trainer._kv_initialized
              assert trainer._update_on_kvstore is True
          except ValueError:
              assert update_on_kv is False
  
      check_trainer_kv_update("default", False)
      check_trainer_kv_update("default", True)
      check_trainer_kv_update("default", None)
      check_trainer_kv_update("row_sparse", False)
      check_trainer_kv_update("row_sparse", True)
      check_trainer_kv_update("row_sparse", None)
      print(
          "worker " + str(my_rank) + " passed test_gluon_trainer_type"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_gluon_trainer_type() without return types