code: def _batch_send_recv_tensor(
      send_tensor_list: Optional[List[torch.Tensor]],
      recv_tensor_metadata: Optional[List[TensorMetadata]],
      send_dst: Optional[int],
      recv_src: Optional[int],
      send_group: Optional[ProcessGroup],
      recv_group: Optional[ProcessGroup],
      current_device: Any,
  ) -> Optional[Union[torch.Tensor, List[torch.Tensor]]]:
      buffer_recv = None
      if recv_tensor_metadata is not None:
          buffer_recv = _create_recv_buffer(
              recv_tensor_metadata, current_device
          )
  
      ops = []
      if send_dst is not None and send_tensor_list is not None:
          assert send_group is not None
          _filling_ops_queue(
              send_tensor_list, dist.isend, send_dst, ops, send_group
          )
      if recv_src is not None and buffer_recv is not None:
          assert recv_group is not None
          _filling_ops_queue(
              buffer_recv, dist.irecv, recv_src, ops, recv_group
          )
  
      if len(ops) > 0:
          reqs = dist.batch_isend_irecv(ops)
          for req in reqs:
              req.wait()
  
      # Remove synchronization according to Pytorch's documentation
      # However, the Megatron-LM does synchronization here
      # https://github.com/microsoft/Megatron-DeepSpeed/blob/ef13d099c2a1609225a4ce4c1a1753cc76dd90a1/megatron/p2p_communication.py#L111-L112
      # In case there is potential error, uncomment the following `torch.cuda.synchronize()`
      # torch.cuda.synchronize()
  
      return buffer_recv
  
imports: import io
  import pickle
  import re
  from collections import namedtuple
  from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.distributed
  from packaging.version import Version
  from torch.distributed import ProcessGroup
  from torch.distributed import distributed_c10d
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from .stage_manager import PipelineStageManager
label_desc: Use image node_id 8 for calling a global function with example usage: _batch_send_recv_tensor(send_tensor_list, recv_tensor_metadata, send_dst, recv_src, send_group, recv_group, current_device) and returns: buffer_recv