code: def customized_distributed_tensor_to_existing_param(
      dtensor: torch.Tensor, param: torch.nn.Parameter
  ):
      """
      Convert the given customized distributed tensor to an existing parameter.
      """
      assert is_customized_distributed_tensor(
          dtensor
      ), "The input tensor is not a customized distributed tensor."
  
      param.data = dtensor.data
      param.shard_fn = dtensor.shard_fn
      param.gather_fn = dtensor.gather_fn
      _hijack_detach_and_clone_for_customized_distributed_tensor(param)
  
imports: import copy
  import operator
  from functools import reduce
  from typing import Union
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from colossalai.device.device_mesh import DeviceMesh
  from .layout import Layout
  from .layout_converter import LayoutConverter
  from .sharding_spec import ShardingSpec
label_desc: Use image node_id 26 for calling a global function with example usage: customized_distributed_tensor_to_existing_param(dtensor, param) without return types