code: def run_tensor_move(rank, world_size, port):
      colossalai.legacy.launch(
          config={},
          rank=0,
          world_size=world_size,
          host="localhost",
          port=port,
          backend="nccl",
      )
  
      src_t = torch.ones(2, 3).cuda()
      tgt_t = torch.zeros(2, 3)
  
      colo_model_data_tensor_move(src_t, tgt_t)
      assert (
          torch.sum(tgt_t) == 6.0
      ), f"{torch.sum(tgt_t.payload)} vs. 6.0"
  
      src_t = torch.ones(2, 3)
      tgt_t = torch.zeros(2, 3).cuda().half()
      colo_model_data_tensor_move(src_t, tgt_t)
      # the src_t has been removed
      assert src_t.numel() == 0
      assert (
          torch.sum(tgt_t) == 6.0
      ), f"{torch.sum(tgt_t.payload)} vs. 6.0"
  
      src_t = ShardedTensor(torch.ones(2, 3))
      tgt_t = ShardedTensor(torch.zeros(2, 3).cuda().half())
      colo_model_data_tensor_move(src_t, tgt_t)
      assert (
          torch.sum(tgt_t.payload) == 6.0
      ), f"{torch.sum(tgt_t.payload)} vs. 6.0"
  
      assert tgt_t.device.type == "cuda"
      colo_model_data_tensor_move_inline(tgt_t, torch.device("cpu"))
      assert tgt_t.device.type == "cpu"
  
imports: import torch
  import colossalai
  from colossalai.legacy.zero.gemini.tensor_utils import colo_model_data_tensor_move, colo_model_data_tensor_move_inline
  from colossalai.legacy.zero.sharded_param import ShardedTensor
  from colossalai.testing import rerun_if_address_is_in_use, spawn
label_desc: Use image node_id 1 for calling a global function with example usage: run_tensor_move(rank, world_size, port) without return types