code: def _communicate_result(result, pg):
      # Gather results from all ranks.
      if result:
          result_tensor = torch.ones(
              1, device=torch.device(torch.cuda.current_device())
          )
      else:
          result_tensor = torch.zeros(
              1, device=torch.device(torch.cuda.current_device())
          )
  
      dist.all_reduce(result_tensor, group=pg)
  
      expected_result = torch.ones(
          1, device=torch.device(torch.cuda.current_device())
      ) * dist.get_world_size(pg)
  
      return torch.equal(result_tensor, expected_result)
  
file_path: pytorch/torch/distributed/_shard/sharded_tensor/_ops/binary_cmp.py
imports: import torch
  import torch.distributed
  import torch.distributed.distributed_c10d
  from torch.distributed._shard.sharded_tensor import ShardedTensor, _sharded_op_impl
label_desc: Use image node_id 1 for calling a global function with example usage: _communicate_result(result, pg) and returns: torch