code: def sync_random_seed(seed=None, device="cuda"):
      """Make sure different ranks share the same seed.
  
      All workers must call this function, otherwise it will deadlock.
      This method is generally used in `DistributedSampler`,
      because the seed should be identical across all processes
      in the distributed group.
  
      In distributed sampling, different ranks should sample non-overlapped
      data in the dataset. Therefore, this function is used to make sure that
      each rank shuffles the data indices in the same order based
      on the same seed. Then different ranks could use different indices
      to select non-overlapped data from the same data list.
  
      Args:
          seed (int, Optional): The seed. Default to None.
          device (str): The device where the seed will be put on.
              Default to 'cuda'.
  
      Returns:
          int: Seed to be used.
      """
      if seed is None:
          seed = np.random.randint(2**31)
      assert isinstance(seed, int)
  
      rank, world_size = get_dist_info()
  
      if world_size == 1:
          return seed
  
      if rank == 0:
          random_num = torch.tensor(
              seed, dtype=torch.int32, device=device
          )
      else:
          random_num = torch.tensor(0, dtype=torch.int32, device=device)
      dist.broadcast(random_num, src=0)
      return random_num.item()
  
file_path: mmdetection/mmdet/utils/dist_utils.py
imports: import functools
  import pickle
  import warnings
  from collections import OrderedDict
  import numpy
  import torch
  import torch.distributed
  from mmengine.dist import get_dist_info
  from torch._utils import _flatten_dense_tensors, _take_tensors, _unflatten_dense_tensors
label_desc: Use image node_id 8 for calling a global function with example usage: sync_random_seed(seed, device) and returns: random_num, seed