code: def get_snode_runtime_for_reorder_compute_test(snode):
      # NOTE: custom cost model to show that the compute reordering algorithm is working
      # Collective kernels
      if isinstance(snode.node, ir.CollectiveKernel):
          if isinstance(snode.node, ir.AllReduce):
              return 100
          else:
              return 100
      elif isinstance(snode.node, ir.Wait):
          return 0
      # High-arithmetic-intensity compute kernels
      elif isinstance(snode.node, ir.ExternKernel):
          return 5
      # All other kernels
      return 1
  
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 1 for calling a global function with example usage: get_snode_runtime_for_reorder_compute_test(snode) and returns: int, int, int, int, int