code: def test_norm_pool_handler():
      model = nn.Sequential(nn.MaxPool2d(4, padding=1).to("meta"))
      tracer = ColoTracer(bias_addition_split=True)
      # graph():
      #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
      #     %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {})
      #     return _0
      meta_args = {"input": torch.rand(4, 4, 64, 64).to("meta")}
      graph = tracer.trace(model, meta_args=meta_args)
  
      gm = ColoGraphModule(model, graph)
      shape_prop_pass(gm, *meta_args.values())
      physical_mesh_id = torch.arange(0, 4)
  
      mesh_shape = (2, 2)
      device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
      conv_mod_node = list(graph.nodes)[1]
      strategies_vector = StrategiesVector(conv_mod_node)
  
      # build handler
      handler = NormPoolingHandler(
          node=conv_mod_node,
          device_mesh=device_mesh,
          strategies_vector=strategies_vector,
      )
      # check operation data mapping
      mapping = handler.get_operation_data_mapping()
  
      for name, op_data in mapping.items():
          op_data: OperationData
          # make sure they have valid values
          assert op_data.data is not None
  
      assert mapping["input"].name == "input_1"
      assert mapping["input"].data.is_meta
      assert mapping["input"].data.shape == torch.Size([4, 4, 64, 64])
      assert mapping["input"].type == OperationDataType.ARG
      assert mapping["input"].logical_shape == torch.Size(
          [4, 4, 64, 64]
      )
  
      assert mapping["output"].name == "_0"
      assert mapping["output"].data.is_meta
      assert mapping["output"].data.shape == torch.Size([4, 4, 16, 16])
      assert mapping["output"].type == OperationDataType.OUTPUT
  
      strategies_vector = handler.register_strategy(
          compute_resharding_cost=False
      )
      strategy_name_list = [val.name for val in strategies_vector]
      assert len(strategy_name_list) == 9
  
file_path: colossalai/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py
imports: import torch
  import torch.nn
  from colossalai._analyzer.fx.graph_module import ColoGraphModule
  from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass
  from colossalai._analyzer.fx.tracer.tracer import ColoTracer
  from colossalai.auto_parallel.tensor_shard.node_handler.normal_pooling_handler import NormPoolingHandler
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.testing import clear_cache_before_run, run_on_environment_flag
label_desc: Use image node_id 1 for calling a global function with example usage: test_norm_pool_handler() without return types