class_docstr: Run correctness checks in multi-proc runner, mark with minimum # GPUs to run under
code: def test_nccl_heuristics(self):
      assert list(baseLat.shape) == [len(NCCL_ALGO), len(NCCL_PROTO)]
      assert list(hwLat.shape) == [
          len(NCCL_HW),
          len(NCCL_ALGO),
          len(NCCL_PROTO),
      ]
      assert llMaxBws.shape[0] == len(NVIDIA_GPU_TYPE)
  
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 8 for calling the TestComputeCommReorderingMultiProc obj's underlying member method code with example usage: obj.test_nccl_heuristics() without return types