code: def run_impl(self, use_fuser):
      warmups = 10
      if self.device == "cuda":
          iters = 1000
      else:
          iters = 10
      engine = tensor_engine.get_engine()
  
      self.bm_jit = None
      for i in range(warmups + iters):
          if i == warmups:
              if self.device == "cuda":
                  engine.sync_cuda()
              time_start = time.time()
  
          if i == 0:
              if self.jit_mode == "trace" and use_fuser:
                  self.bm_jit = torch.jit.trace(
                      self.forward,
                      example_inputs=self.inputs,
                      check_trace=False,
                  )
              if callable(getattr(self, "reference", None)):
                  self.check()
              else:
                  print(
                      "Warning: no reference result for ", self.module()
                  )
          elif i == 1:
              # The fusion graph is visible after the first iter is executed
              if (
                  self.jit_mode == "trace"
                  and use_fuser
                  and self.print_ir
              ):
                  print(self.bm_jit.graph_for(*self.inputs))
          z = self.compute()
          if self.mode == "both":
              if self.result_grad is None:
                  self.result_grad = engine.rand_like(z)
              engine.backward(
                  [z], [self.result_grad], self.grad_variables
              )
  
      if self.device == "cuda":
          engine.sync_cuda()
  
      duration = time.time() - time_start
      iter_time = duration / iters
      memory_workload = self.memory_workload()
      compute_workload = self.compute_workload()
  
      result_dict = {
          "desc": self.desc(),
          "us": iter_time * 1e6,
          "sol": memory_workload["sol"]
          * self.dtype_to_bytes()
          / iter_time
          / 1e9,
          "algorithmic": memory_workload["algorithmic"]
          * self.dtype_to_bytes()
          / iter_time
          / 1e9,
      }
      if compute_workload:
          result_dict["compute_workload"] = (
              compute_workload / iter_time / 1e9
          )
      self.dump_result(result_dict)
  
imports: import contextlib
  import json
  import os
  import time
  import numpy
  import torch
  from .None import tensor_engine
label_desc: Use image node_id 17 for calling the Benchmark obj's underlying member method code with example usage: obj.run_impl(use_fuser) without return types