code: def test_where_handler():
      model = ConvModel()
      tracer = ColoTracer(bias_addition_split=True)
      # graph():
      #     %condition : torch.Tensor [#users=1] = placeholder[target=condition]
      #     %x : torch.Tensor [#users=1] = placeholder[target=x]
      #     %y : torch.Tensor [#users=1] = placeholder[target=y]
      #     %where : [#users=1] = call_function[target=torch.where](args = (%condition, %x, %y), kwargs = {})
      #     return where
      meta_args = {
          "condition": torch.rand(4, 4, 64, 64).to("meta"),
          "x": torch.rand(4, 1, 64, 64).to("meta"),
          "y": torch.rand(1, 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)
      where_node = list(graph.nodes)[3]
      strategies_vector = StrategiesVector(where_node)
  
      # build handler
      handler = WhereHandler(
          node=where_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.logical_shape is not None
          assert op_data.data is not None
  
      assert mapping["condition"].name == "condition"
      assert mapping["condition"].data.is_meta
      assert mapping["condition"].data.shape == torch.Size(
          [4, 4, 64, 64]
      )
      assert mapping["condition"].type == OperationDataType.ARG
      assert mapping["condition"].logical_shape == torch.Size(
          [4, 4, 64, 64]
      )
  
      assert mapping["x"].name == "x"
      assert mapping["x"].data.is_meta
      assert mapping["x"].data.shape == torch.Size([4, 1, 64, 64])
      assert mapping["x"].type == OperationDataType.ARG
      assert mapping["x"].logical_shape == torch.Size([4, 4, 64, 64])
  
      assert mapping["y"].name == "y"
      assert mapping["y"].data.is_meta
      assert mapping["y"].data.shape == torch.Size([1, 4, 64, 64])
      assert mapping["y"].type == OperationDataType.ARG
      assert mapping["y"].logical_shape == torch.Size([4, 4, 64, 64])
  
      assert mapping["output"].name == "where"
      assert mapping["output"].data.is_meta
      assert mapping["output"].data.shape == torch.Size([4, 4, 64, 64])
      assert mapping["output"].type == OperationDataType.OUTPUT
  
      handler.register_strategy(compute_resharding_cost=False)
      strategy_name_list = [val.name for val in strategies_vector]
      # 4*3 + 4*3/2*2 + 1
      assert len(strategy_name_list) == 25
  
file_path: ColossalAI/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py
imports: import pytest
  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.where_handler import WhereHandler
  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
label_desc: Use image node_id 1 for calling a global function with example usage: test_where_handler() without return types