code: def move(tensor, device):
      if tensor.is_meta:
          return torch.empty_like(tensor, device=device)
      else:
          # Using new tensors help in freeing memory (after split for example) was done before by calling clone().
          # Using copy=True instead of clone() will help in case of cpu --> cpu.
          # Otherwise to() will not create a new copy for the view of the full tensor, and it will not be de-referenced.
          return tensor.to(device, copy=True)
  
imports: import re
  from torch import nn
  from .replace_policy import replace_policies
  from typing import Optional
  import torch
  from deepspeed import comm
  from .layers import LinearAllreduce, LinearLayer, LmHeadLinearAllreduce
  from deepspeed.accelerator import get_accelerator
  from .fusedqkv_utils import require_tp_fused_qkvw, prepare_tp_fused_qkvw
  from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
label_desc: Use image node_id 1 for calling a global function with example usage: move(tensor, device) and returns: torch, tensor