⚡️ Speed up method AlexNet._extract_features by 623%

Here is an optimized version of your program.
- I've replaced the `for i in range(len(x)):` with a more Pythonic and efficient way using `range` only if needed, or better, used no-ops directly since the for loop is entirely a no-op (`pass` inside).
- If `x` is large, looping does use CPU; `result` is always an empty list, so the function may as well skip unnecessary looping.

If the logic is meant to be a placeholder, it can be reduced to a minimal form that does the same work.



**If you expect to implement something in the loop later, but for now want it fast, this is optimal.**

If you are actually meant to process `x` later, let me know what it should do, but as written, your function just always returns an empty list, and the for loop is just wasted runtime.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -31,11 +31,8 @@ def forward(self, x):
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # The loop and result are a no-op, just return empty list
+        return []
 
     def _classify(self, features):
         total_mod = sum(features) % self.num_classes