class_docstr: Simplified version of Triton autotuner that has no invalidation
  key and caches the best config to disk to improve cold start times.
  Unlike the main triton Autotuner, this version can precompile all
  configs, and does not rely on the Triton JIT.
class_docstr_tok: ["Simplified","version","of","Triton","autotuner","that","has","no","invalidation","key","and","caches","the","best","config","to","disk","to","improve","cold","start","times",".","Unlike","the","main","triton","Autotuner",",","this","version","can","precompile","all","configs",",","and","does","not","rely","on","the","Triton","JIT","."]
code: def clone_args(
      self, *args, **kwargs
  ) -> Tuple[List[Any], Dict[str, Any]]:
      from .compile_fx import clone_preserve_strides
  
      # clone inplace buffers to avoid autotune contaminating them if
      # the kernel does in-place stores. avoid cloning other buffers because
      # it leads to increase memory use
      cloned_args = []
      for i, arg in enumerate(args):
          if self.fn.arg_names[i] in self.mutated_arg_names:
              assert isinstance(arg, torch.Tensor)
              cloned_args.append(clone_preserve_strides(arg))
          else:
              cloned_args.append(arg)
  
      cloned_kwargs: Dict[str, Any] = {}
      for name, arg in kwargs.items():
          if name in self.mutated_arg_names:
              assert isinstance(arg, torch.Tensor)
              cloned_kwargs[name] = clone_preserve_strides(arg)
          else:
              cloned_kwargs[name] = arg
  
      return cloned_args, cloned_kwargs
  
imports: import builtins
  import copy
  import functools
  import hashlib
  import inspect
  import json
  import logging
  import math
  import operator
  import os
  import os.path
  import re
  import threading
  from enum import auto, Enum
  from typing import Any, Callable, Dict, List, Optional, Set, Tuple
  import torch
  import torch.autograd.profiler
  from torch._dynamo.device_interface import get_interface_for_device
  from torch._dynamo.utils import dynamo_timed, get_first_attr
  from torch.utils._triton import has_triton_package
  from .None import config
  from .codecache import cache_dir, CudaKernelParamCache
  from .coordinate_descent_tuner import CoordescTuner
  from .ir import ReductionHint, TileHint
  from .utils import ceildiv, conditional_product, create_bandwidth_info_str, do_bench, get_num_bytes, next_power_of_2, triton_config_to_hashable
label_desc: Use image node_id 5 for calling the CachingAutotuner obj's underlying member method code with example usage: obj.clone_args() and returns: cloned_args,  cloned_kwargs