code: def autotune_hints_to_configs(
      hints: Set[AutotuneHint], size_hints, block_size: int
  ) -> List[Config]:
      """
      AutotuneHints can be attached to the metadata of triton kernels for providing
      suggestions about what to try for autotuning. One reason to do this is if there are
      some configs that are only useful in specific scenarios, in which case we can avoid
      wasting compile time on autotuning unless we know we are in one of those scenarios.
  
      Based on those hints, this function will generate a list of additional autotuning
      configs to try.
      """
      xyz_options: Tuple[Tuple[int, Optional[int], Optional[int]], ...]
      configs = []
  
      for hint in hints:
          if hint == AutotuneHint.ELEMENTS_PER_WARP_32:
              if len(size_hints) == 1:
                  xyz_options = ((block_size // 4, None, None),)
              elif len(size_hints) == 2:
                  xyz_options = (
                      (block_size // 4, 1, None),
                      (1, block_size // 4, None),
                  )
              elif len(size_hints) == 3:
                  xyz_options = (
                      (block_size // 4, 1, 1),
                      (1, block_size // 4, 1),
                      (1, 1, block_size // 4),
                  )
              for xyz in xyz_options:
                  configs.append(
                      triton_config(
                          size_hints,
                          *xyz,
                          num_elements_per_warp=32,
                      )
                  )
  
      return configs
  
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 1 for calling a global function with example usage: autotune_hints_to_configs(hints, size_hints, block_size) and returns: configs