⚡️ Speed up method AlexNet._extract_features by 663%

Here's the **optimized version** of your code.  
Your original for-loop only iterated and did nothing (contained just `pass`). To optimize such a case, **do not loop at all**—the loop is entirely unnecessary and is the biggest cost observed in the profile.  
If this loop is a placeholder for future feature extraction (the "real" code), you should only optimize so far as this placeholder allows.  
But based on what's given, here's the more efficient version (no-op extraction).



**Explanation**.
- The original method performed no computation, just created and returned an empty list after looping over input.
- The optimized version immediately returns the empty list, entirely eliminating the unnecessary loop.

This is now O(1) runtime regardless of `x`.  
**Line profile time will no longer be spent inside the unusable loop.**

If in the future you add real feature extraction inside the loop, consider vectorized operations with NumPy or appropriate PyTorch/TensorFlow ops to optimize further. Let me know if you need help with that!

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -29,15 +29,12 @@ def forward(self, x):
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # As no operation is being performed, return an empty list directly
+        return []
 
     def _classify(self, features):
-        total = sum(features)
-        return [total % self.num_classes for _ in features]
+        total_mod = sum(features) % self.num_classes
+        return [total_mod] * len(features)
 
 
 class SimpleModel: