code: def sync_tensor(flat_tensor, tensor_list):
      """
      Synchronize the flattened tensor and unflattened tensor list. When
      a list of tensor are flattened with `torch._utils._unflatten_dense_tensors`,
      a new tensor is created. Thus, the flat tensor and original tensor list do not
      share the same memory space. This function will update the tensor list so that
      they point to the same value.
  
      :param flat_tensor: A flat tensor obtained by calling `torch._utils._unflatten_dense_tensors` on a tensor list
      :param tensor_list: A list of tensors corresponding to the flattened tensor
      :type flat_tensor: torch.Tensor
      :type tensor_list: List[torch.Tensor]
      """
      updated_params = unflatten(flat_tensor, tensor_list)
  
      # update the tensor data
      for p, q in zip(tensor_list, updated_params):
          p.data = q.data
  
file_path: colossalai/colossalai/zero/low_level/_utils.py
imports: import math
  from typing import Optional
  import torch
  import torch.distributed
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
label_desc: Use image node_id 14 for calling a global function with example usage: sync_tensor(flat_tensor, tensor_list) without return types