⚡️ Speed up method AlexNet._extract_features by 754%

Here’s an optimized version of your code. The original `_extract_features` method is an (empty) O(N) for-loop, which is essentially a waste if the real feature extraction logic is not provided. For demonstration, I'll keep the functionally-correct placeholder, but will replace the loop with a slice if you intend to return an empty list of the same (zero) behavior, improving speed.



**Explanation:**  
- The for loop, as written, does nothing except iterate and burn time.  
- Returning an empty list is equivalent to the old function.
- No for-loop is needed, yielding fastest runtime and memory usage for this specific logic.
- All comments are preserved unless modified for clarity or due to code change.

**If you do have real feature extraction logic**, paste that for further optimization of the computational part. As profiled, your bottleneck was the unnecessary for-loop.

This is fully optimal for the code as posted.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -29,11 +29,8 @@ def forward(self, x):
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # Directly return empty result, equivalent to the original code
+        return []
 
     def _classify(self, features):
         total_mod = sum(features) % self.num_classes