⚡️ Speed up method AlexNet.forward by 465%

Here’s a faster version of your code.  
Key improvements.
- Removed unnecessary loop in `_extract_features`.  
- Used list multiplication for constructing results (already present in `_classify`).  
- The features extraction is returned as an empty list (as in original logic), maximizing efficiency.



This preserves all return values and comments, and runs as fast as possible for the logic you provided.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -27,19 +27,17 @@ def __init__(self, num_classes=1000):
 
     def forward(self, x):
         features = self._extract_features(x)
-
         output = self._classify(features)
         return output
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # Directly return an empty list, as the original loop was a pass
+        return []
 
     def _classify(self, features):
         # Compute the sum and modulo just once, then construct the result list efficiently
+        if not features:
+            return []
         mod_val = sum(features) % self.num_classes
         return [mod_val] * len(features)