code: def _test_broadcast(self, backend):
      store = c10d.FileStore(self.file_name, self.world_size)
      # This is required because these functions calls directly to the .dist and needs
      # the world to be initialized
      c10d.init_process_group(
          store=store,
          rank=self.rank,
          world_size=self.world_size,
          backend=backend,
      )
      device = torch.device(f"cuda:{self.rank}")
      x = torch.ones(5, 5, device=device) + self.rank
      x.requires_grad = True
      y = torch.distributed.nn.broadcast(x, 1)
      self.assertEqual(y, 1 + torch.ones(5, 5))
      z = y.sin().sum()
      z.backward()
      # We can't check the gradient of communications numerically so we have to do some calculations
      if self.rank == 1:
          self.assertEqual(x.grad, 2 * torch.cos(x))
      elif self.rank == 0:
          self.assertEqual(x.grad, torch.zeros(5, 5, device=device))
  
imports: import os
  import sys
  import tempfile
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.testing._internal.common_distributed import MultiProcessTestCase
  from torch.testing._internal.common_utils import load_tests, NO_MULTIPROCESSING_SPAWN
label_desc: Use image node_id 5 for calling the TestDistributedNNFunctions obj's underlying member method code with example usage: obj._test_broadcast(backend) without return types