class_docstr: Run correctness checks in multi-proc runner, mark with minimum # GPUs to run under
code: def test_sink_waits(self):
      def func(a, *, tag, ranks, group_size):
          ar = _functional_collectives.all_reduce(a, "sum", ranks, tag)
          c = torch.relu(a)
          d = torch.matmul(c, c)
          e = d + ar
          return (e,)
  
      with _dynamo_dist_per_rank_init(self.rank, self.world_size):
          inputs = (
              torch.ones(4, 4, dtype=torch.float, device="cuda")
              + self.rank
          )
          compiled = torch.compile(func)
          code = run_and_get_triton_code(
              compiled, inputs, **self.get_world_trs()
          )
          # NOTE: notice that `_wait_tensor` is delayed until right before first use
          FileCheck().check("dist.all_reduce(").check(
              "triton_poi_fused_relu"
          ).check("_wait_tensor(").run(code)
          out = compiled(inputs, **self.get_world_trs())
          correct = func(inputs, **self.get_world_trs())
          self.assertTrue(same(out, correct))
  
imports: import unittest
  from unittest.mock import patch
  import torch
  import torch._dynamo
  import torch._dynamo.logging
  import torch._dynamo.test_case
  import torch.distributed._functional_collectives
  from torch._C import FileCheck
  from torch._dynamo.utils import same
  from torch._inductor import ir
  from torch._inductor.comm_analysis import baseLat, hwLat, llMaxBws, NCCL_ALGO, NCCL_HW, NCCL_PROTO, NVIDIA_GPU_TYPE
  from torch._inductor.utils import run_and_get_triton_code
  from torch.testing._internal.common_distributed import _dynamo_dist_per_rank_init, DynamoDistributedMultiProcTestCase, requires_nccl, skip_if_lt_x_gpu
  from torch.utils._triton import has_triton
label_desc: Use image node_id 3 for calling the TestComputeCommReorderingMultiProc obj's underlying member method code with example usage: obj.test_sink_waits() and returns: e