class_docstr: The coordinate descent tuner. Tune one field/coordinate at a time.
  
  TODO will it be necessary to tune multiple fields simultaneously.
  
  
  TODO: what if both increasing and decreasing a field can improve perf.
        i.e., there are multiple local optima..
class_docstr_tok: ["The","coordinate","descent","tuner",".","Tune","one","field\/coordinate","at","a","time",".","TODO","will","it","be","necessary","to","tune","multiple","fields","simultaneously",".","TODO",":","what","if","both","increasing","and","decreasing","a","field","can","improve","perf",".","i.e.",",","there","are","multiple","local","optima",".."]
code: def autotune(
      self,
      func: Callable[["triton.Config"], float],
      baseline_config: "triton.Config",
      baseline_timing: Optional[float] = None,
  ) -> "triton.Config":
      if baseline_timing is None:
          baseline_timing = self.call_func(func, baseline_config)
  
      log.debug("= Do coordinate descent tuning for %s =", self.name)
      log.debug(
          "Baseline Config %s, baseline timing %f",
          baseline_config,
          baseline_timing,
      )
      improved = True
      best_config = baseline_config
      best_timing = baseline_timing
      tunable_fields = self.tunable_fields
  
      while improved:
          improved = False
  
          for name in tunable_fields:
              cur_val = get_field(best_config, name)
              # some kernel don't have RBLOCK/YBLOCK/ZBLOCK. So cur_val may be None
              if cur_val is None:
                  continue
  
              # It's possible that candidate_values is empty.
              # E.g., if XBLOCK is 1 initially and size_hint for x is also 1.
              # We would not try either larger or smaller XBLOCK in this case.
              candidate_values = self.get_neighbour_values(
                  name, cur_val
              )
  
              for next_val in candidate_values:
                  candidate_config = copy.deepcopy(best_config)
                  set_field(candidate_config, name, next_val)
  
                  cmp_res, candidate_timing = self.compare_config(
                      func, candidate_config, best_config, best_timing
                  )
                  if cmp_res:
                      improved = True
                      best_config, best_timing = (
                          candidate_config,
                          candidate_timing,
                      )
  
          if (
              not improved
              and inductor_config.coordinate_descent_check_all_directions
          ):
              old_best_timing = best_timing
              (
                  improved,
                  best_config,
                  best_timing,
              ) = self.check_all_tuning_directions(
                  func, best_config, best_timing
              )
  
              if improved:
                  msg = red_text(
                      "Coordinate descend tuning found improvement of %.3fx by looking in all directions."
                  )
                  log.debug(
                      msg,
                      old_best_timing / best_timing,
                  )
  
      log.debug(
          "Improve from %s %f -> %s %f, %.3fx",
          baseline_config,
          baseline_timing,
          best_config,
          best_timing,
          baseline_timing / best_timing,
      )
  
      return best_config
  
imports: import copy
  import itertools
  import logging
  from typing import Callable, Optional
  from torch.utils._triton import has_triton
  from .utils import red_text, triton_config_to_hashable
  from .None import config
label_desc: Use image node_id 16 for calling the CoordescTuner obj's underlying member method code with example usage: obj.autotune(func, baseline_config, baseline_timing) and returns: best_config