code: def _serialize_to_tensor(data, group):
      """
      Seriialize the tensor to ByteTensor. Note that only `gloo` and `nccl`
          backend is supported.
      Args:
          data (data): data to be serialized.
          group (group): pytorch dist group.
      Returns:
          tensor (ByteTensor): tensor that serialized.
      """
  
      backend = dist.get_backend(group)
      assert backend in ["gloo", "nccl"]
      device = torch.device("cpu" if backend == "gloo" else "cuda")
  
      buffer = pickle.dumps(data)
      if len(buffer) > 1024**3:
          logger = logging.getLogger(__name__)
          logger.warning(
              "Rank {} trying to all-gather {:.2f} GB of data on device {}".format(
                  get_rank(), len(buffer) / (1024**3), device
              )
          )
      storage = torch.ByteStorage.from_buffer(buffer)
      tensor = torch.ByteTensor(storage).to(device=device)
      return tensor
  
imports: import functools
  import logging
  import pickle
  import torch
  import torch.distributed
  from pytorchvideo.layers.distributed import cat_all_gather, get_local_process_group, get_local_rank, get_local_size, get_world_size, init_distributed_training
label_desc: Use image node_id 10 for calling a global function with example usage: _serialize_to_tensor(data, group) and returns: tensor