def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False):
    """
	Decorator for auto-tuning a :code:`triton.jit`'d function.
	.. highlight:: python
	.. code-block:: python
		@triton.autotune(configs=[
			triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
			triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
			],
			key=['x_size'] # the two above configs will be evaluated anytime
							# the value of x_size changes
		)
		@triton.jit
		def kernel(x_ptr, x_size, **META):
			BLOCK_SIZE = META['BLOCK_SIZE']
	:note: When all the configurations are evaluated, the kernel will run multiple time.
			This means that whatever value the kernel updates will be updated multiple times.
			To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
			reset the value of the provided tensor to `zero` before running any configuration.
	:param configs: a list of :code:`triton.Config` objects
	:type configs: list[triton.Config]
	:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
	:type key: list[str]
	:param prune_configs_by: a dict of functions that are used to prune configs, fields:
		'perf_model': performance model used to predicate running time with different configs, returns running time
		'top_k': number of configs to bench
		'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
	:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
	:type reset_to_zero: list[str]
	"""

    def decorator(fn):
        return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, prune_configs_by, nearest_power_of_two)

    return decorator

	Decorator for auto-tuning a :code:`triton.jit`'d function.
	.. highlight:: python
	.. code-block:: python
		@triton.autotune(configs=[
			triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
			triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
			],
			key=['x_size'] # the two above configs will be evaluated anytime
							# the value of x_size changes
		)
		@triton.jit
		def kernel(x_ptr, x_size, **META):
			BLOCK_SIZE = META['BLOCK_SIZE']
	:note: When all the configurations are evaluated, the kernel will run multiple time.
			This means that whatever value the kernel updates will be updated multiple times.
			To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
			reset the value of the provided tensor to `zero` before running any configuration.
	:param configs: a list of :code:`triton.Config` objects
	:type configs: list[triton.Config]
	:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
	:type key: list[str]
	:param prune_configs_by: a dict of functions that are used to prune configs, fields:
		'perf_model': performance model used to predicate running time with different configs, returns running time
		'top_k': number of configs to bench
		'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
	:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
	:type reset_to_zero: list[str]
	
quant/custom_autotune.py