code: def _test_all_to_all(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}")
      x0 = torch.ones(5, 5, device=device) + 2 * self.rank
      x1 = torch.ones(5, 5, device=device) + 2 * self.rank
      x0.requires_grad = True
      x1.requires_grad = True
      y0 = torch.empty_like(x0)
      y1 = torch.empty_like(x1)
      tensors = torch.distributed.nn.all_to_all([y0, y1], [x0, x1])
      for i, t in enumerate(tensors):
          self.assertEqual(t, torch.ones(5, 5, device=device) + 2 * i)
      y = torch.sum(torch.stack(tensors), axis=0)
      z = y.sin().sum()
      z.backward()
      x_s = (4 * torch.ones(5, 5, device=device)).cos()
      self.assertEqual(x0.grad, x_s)
      self.assertEqual(x1.grad, x_s)
  
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 9 for calling the TestDistributedNNFunctions obj's underlying member method code with example usage: obj._test_all_to_all(backend) without return types