code: def copy_tensor(dst_tensor, src_tensor):
      """Copy the content from src_tensor to dst_tensor.
  
      Args:
          dst_tensor: the tensor to copy from.
          src_tensor: the tensor to copy to.
  
      Returns:
          None
      """
      copied = True
      if isinstance(dst_tensor, numpy.ndarray) and isinstance(
          src_tensor, numpy.ndarray
      ):
          numpy.copyto(dst_tensor, src_tensor)
      elif torch_available():
          if isinstance(dst_tensor, torch.Tensor) and isinstance(
              src_tensor, torch.Tensor
          ):
              dst_tensor.copy_(src_tensor)
          elif isinstance(dst_tensor, torch.Tensor) and isinstance(
              src_tensor, numpy.ndarray
          ):
              t = torch.Tensor(src_tensor)
              dst_tensor.copy_(t)
          elif isinstance(dst_tensor, numpy.ndarray) and isinstance(
              src_tensor, torch.Tensor
          ):
              t = src_tensor.numpy()
              numpy.copyto(dst_tensor, t)
          else:
              copied = False
      else:
          copied = False
      if not copied:
          raise ValueError(
              "Unsupported tensor type. Got: {} and {}. Supported "
              "CPU tensor types are: torch.Tensor, numpy.ndarray.".format(
                  type(dst_tensor), type(src_tensor)
              )
          )
  
imports: import asyncio
  import time
  from typing import List
  import numpy
  import ray
  import ray.experimental.internal_kv
  from ray._raylet import GcsClient
  from ray.util.collective.types import ReduceOp, torch_available
  from ray.util.queue import _QueueActor
label_desc: Use image node_id 10 for calling a global function with example usage: copy_tensor(dst_tensor, src_tensor) without return types