code: def run(self, args):
      self.print_ir = args.print_ir
      if args.cuda_fuser == "old":
          torch._C._jit_override_can_fuse_on_gpu(True)
          if args.print_kernel:
              os.environ["PYTORCH_FUSION_DEBUG"] = "1"
          return self.run_impl(True)
      elif args.cuda_fuser == "te":
          torch._C._jit_set_texpr_fuser_enabled(True)
          with cuda_pointwise_context(
              args.cuda_pointwise_loop_levels,
              args.cuda_pointwise_block_count,
              args.cuda_pointwise_block_size,
          ):
              return self.run_impl(True)
      elif args.cuda_fuser == "nvf":
          torch._C._jit_set_nvfuser_enabled(True)
          torch._C._jit_set_profiling_executor(True)
          torch._C._jit_set_profiling_mode(True)
          torch._C._jit_override_can_fuse_on_cpu(False)
          torch._C._jit_override_can_fuse_on_gpu(False)
          torch._C._jit_set_bailout_depth(20)
          if args.print_kernel:
              os.environ["PYTORCH_CUDA_FUSER_DEBUG"] = "1"
          return self.run_impl(True)
      else:
          return self.run_impl(False)
  
imports: import contextlib
  import json
  import os
  import time
  import numpy
  import torch
  from .None import tensor_engine
label_desc: Use image node_id 16 for calling the Benchmark obj's underlying member method code with example usage: obj.run(args) and returns: self, self, self, self