diff --git a/onnxscript/optimizer/__init__.py b/onnxscript/optimizer/__init__.py
index d64a69d1c..e643762aa 100644
--- a/onnxscript/optimizer/__init__.py
+++ b/onnxscript/optimizer/__init__.py
@@ -129,6 +129,20 @@ def optimize_ir(
     input_size_limit: int = _DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
     output_size_limit: int = _DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT,
 ) -> None:
+    """
+    Optimizes a model.
+
+    Args:
+        model: The model to be optimized
+        num_iterations: Number of times the optimization loop is repeated
+        onnx_shape_inference: Applies node-level shape-inference as part of optimization
+        input_size_limit: Will not apply constant folding to ops with any input of size
+            greater than this. Does not apply to special ops like Shape() and Size().
+        output_size_limit: Will not rewrite any foldable-op into a Constant op if the size
+            of the output tensor is greater than this.
+        stop_if_no_change: Not supported currently (has no effect). Meant to stop the
+            outer optimization loop if no change is detected in one iteration.
+    """
     del stop_if_no_change  # Looks like rewriter doesn't support this yet.
     _inliner.inline(model)
     for _ in range(num_iterations):
diff --git a/onnxscript/optimizer/_constant_folding.py b/onnxscript/optimizer/_constant_folding.py
index 3e210ce59..5d76379cb 100644
--- a/onnxscript/optimizer/_constant_folding.py
+++ b/onnxscript/optimizer/_constant_folding.py
@@ -707,7 +707,9 @@ def process_node(self, node: ir.Node):
             if logger.isEnabledFor(logging.DEBUG):
                 input_sizes = [input.size for input in input_values]
                 logger.debug(
-                    f"Skipping constant folding for op {node.op_type} due to large input size: {input_sizes}"
+                    "Skipping constant folding for op %s due to large input size: %s",
+                    node.op_type,
+                    input_sizes,
                 )
             return None