diff --git a/python/triton/runtime/autotuner.py b/python/triton/runtime/autotuner.py
index 0a6394ac1e82..3cb9f9dbe862 100644
--- a/python/triton/runtime/autotuner.py
+++ b/python/triton/runtime/autotuner.py
@@ -142,6 +142,7 @@ def warmup(self, *args, **kwargs):
 class Config:
     """
     An object that represents a possible kernel configuration for the auto-tuner to try.
+
     :ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments.
     :type meta: dict[Str, Any]
     :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if
@@ -173,8 +174,10 @@ def __str__(self):
 def autotune(configs, key, prune_configs_by=None, reset_to_zero=None):
     """
     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),
@@ -223,8 +226,10 @@ def heuristics(values):
     """
     Decorator for specifying how the values of certain meta-parameters may be computed.
     This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable.
+
     .. highlight:: python
     .. code-block:: python
+
         @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))})
         @triton.jit
         def kernel(x_ptr, x_size, **META):