⚡️ Speed up method AlexNet._extract_features by 861%

Here is an optimized version of your program. The original `_extract_features` method is just a skeleton and does nothing, so there is nothing to optimize in the logic. However, to make this code more performant (while still functionally identical), I'll.

- Avoid redundant calls to `len(x)` in each iteration.
- Use a faster list construction (e.g., using list comprehension if computation is added later).
- Predeclare `result` with a fixed size if possible, but since the body is empty, just optimize for speed and clarity.
- Use `range(len(x))` only once, as there's no other logic.

Rewritten code.



This preserves the return type (a list the same length as `x`, as in your original placeholder), AND runs the loop at C-speed. If you want to maintain the exact semantics ("empty list"), you can simply do.



**However:**  
The current logic always returns an empty list, independent of input. If you want a placeholder that iterates as in your original, the list comprehension is fastest.

If your intention is to fill or process `x`, add that logic! Otherwise, the above is the most efficient translation.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -30,11 +30,8 @@ def forward(self, x):
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # No-op: returns an empty list as in the original code
+        return []
 
     def _classify(self, features):
         total_mod = sum(features) % self.num_classes