class_docstr: Run correctness checks in multi-proc runner, mark with minimum # GPUs to run under
code: def test_permute_tensor(self):
      def func(tensor, src_dst_pairs, *, tag, ranks, group_size):
          return _functional_collectives.permute_tensor(
              tensor, src_dst_pairs, ranks, tag
          )
  
      with _dynamo_dist_per_rank_init(self.rank, self.world_size):
          inputs = (
              # rank0: [0., 1.], rank1: [2., 3.]
              torch.arange(2, dtype=torch.float32, device="cuda")
              + 2 * self.rank,
              [1, 0],
          )
          compiled = torch.compile(func)
          out = compiled(*inputs, **self.get_world_trs())
          correct = func(*inputs, **self.get_world_trs())
          self.assertTrue(same(out, correct))
  
          # rank0: [2., 3.], rank1: [0., 1.]
          expected = torch.arange(
              2, dtype=torch.float32, device="cuda"
          ) + 2 * ((self.rank - 1 + self.world_size) % self.world_size)
          self.assertEqual(out, expected)
          self.assertEqual(correct, expected)
  
imports: import functools
  import unittest
  from unittest.mock import patch
  import torch
  from torch._C import FileCheck
  import torch.distributed._functional_collectives
  import torch._dynamo
  import torch._dynamo.test_case
  from torch._dynamo.utils import same
  from torch._dynamo.testing import CompileCounter
  from torch.distributed.distributed_c10d import GroupMember
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing._internal.common_distributed import DynamoDistributedSingleProcTestCase, DynamoDistributedMultiProcTestCase, _dynamo_dist_per_rank_init, requires_nccl, skip_if_lt_x_gpu
  from torch._inductor.compile_fx import compile_fx
  from torch.utils._triton import has_triton
  from torch._inductor.utils import run_and_get_triton_code
  import torch._dynamo.logging
label_desc: Use image node_id 9 for calling the TestCollectivesMultiProc obj's underlying member method code with example usage: obj.test_permute_tensor() and returns: _functional_collectives