code: def share_tensor_via_shm(
      tensor: Optional[torch.Tensor], src_rank: int = 0
  ) -> torch.Tensor:
      """
      Share a tensor via shared memory with local peers.
      This is a collective function that must be called by all processes within
      the global process group. Rank `src_rank` must pass in the tensor it wants
      to share.
      NOTE: this is a simple implementation that only supports the single-host,
      multi-process environment. Multi-host support is possible but is slightly
      more complicated.
  
      See [`torch.multiprocessing`](https://pytorch.org/docs/stable/multiprocessing.html) and
      [best practices](https://pytorch.org/docs/1.6.0/notes/multiprocessing.html?highlight=multiprocessing) for more information on shared memory
  
      Args:
          tensor: The tensor to share.
          src_rank: The rank sharing the tensor.
      Returns:
          The tensor shared via shared memory.
  
      Example::
  
          dist.init_process_group(backend="nccl")
          local_rank = int(os.environ["LOCAL_RANK"])
          device = torch.device(f"cuda:{local_rank}")
          torch.cuda.set_device(device)
          dist.barrier()
  
          if dist.get_rank() == 0:
              # Pretend that we are loading the pretrained embedding weight from a parquet file on rank 0.
              emb = torch.rand(2000000, 64)
              # Share the tensor to local peers via shared memory
              emb = share_tensor_via_shm(tensor=emb)
          else:
              # Received the tensor shared by rank 0 via shared memory
              emb = share_tensor_via_shm(tensor=None)
  
          assert emb.is_shared()
      """
  
      if not dist.is_initialized():
          raise RuntimeError("Global process group is not initialized")
  
      global gloo_pg
      if gloo_pg is None:
          if dist.get_backend() == "gloo":
              gloo_pg = dist.group.WORLD
          else:
              gloo_pg = dist.new_group(backend="gloo")
  
      torch.multiprocessing.set_sharing_strategy("file_system")
  
      if dist.get_rank() == src_rank:
          assert (
              tensor is not None
          ), f"src_rank ({src_rank}) must provide a tensor"
  
          # Intialize a custom pickler
          buf = io.BytesIO()
          shm_pickler = pickle.Pickler(buf)
          shm_pickler.dispatch_table = copyreg.dispatch_table.copy()
  
          # Register reducers for moving the tensor storage to shared memory
          for t in torch._storage_classes:
              if t.__name__ == "_UntypedStorage":
                  # pyre-ignore [16]
                  shm_pickler.dispatch_table[t] = reduce_storage
              else:
                  shm_pickler.dispatch_table[
                      t
                  ] = reduce_typed_storage_child
          # pyre-fixme[16]: Module `storage` has no attribute `_TypedStorage`.
          shm_pickler.dispatch_table[
              torch.storage._TypedStorage
          ] = reduce_typed_storage
  
          tensor.share_memory_()
          shm_pickler.dump(tensor)
  
          obj_list = [buf.getvalue()]
          dist.broadcast_object_list(
              obj_list, src=src_rank, group=gloo_pg
          )
          dist.barrier(group=gloo_pg)
          return tensor
      else:
          obj_list = [None]
          dist.broadcast_object_list(
              obj_list, src=src_rank, group=gloo_pg
          )
          obj = obj_list[0]
          assert obj is not None
          buf = io.BytesIO(obj)
          dist.barrier(group=gloo_pg)
          return pickle.load(buf)
  
imports: import copyreg
  import io
  import os
  import pickle
  import uuid
  from typing import cast, List, Optional
  import torch
  import torch.distributed
  import torch.distributed.launcher
  import torchrec
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torch import nn
  from torch.multiprocessing.reductions import reduce_storage, reduce_typed_storage, reduce_typed_storage_child
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.types import ParameterConstraints
  from torchrec.distributed.types import ModuleSharder, ShardingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
label_desc: Use image node_id 1 for calling a global function with example usage: share_tensor_via_shm(tensor, src_rank) and returns: tensor, pickle