class_docstr: Run correctness checks in multi-proc runner, mark with minimum # GPUs to run under
code: def test_c10d_functional_tagged_pt2_compliant(self):
      op = torch.ops._c10d_functional.all_reduce.default
      self.assertIn(torch.Tag.pt2_compliant_tag, op.tags)
      op = torch.ops.c10d_functional.all_reduce.default
      self.assertIn(torch.Tag.pt2_compliant_tag, op.tags)
  
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 5 for calling the TestCollectivesMultiProc obj's underlying member method code with example usage: obj.test_c10d_functional_tagged_pt2_compliant() without return types