⚡️ Speed up method AlexNet._classify by 391%

Certainly! Below is a faster version of your program. It mainly optimizes the `_classify` method by avoiding recomputation of the same expression in the list comprehension and leveraging local variables to reduce attribute lookup overhead (Python does this faster).



**Explanation:**
- Precompute `sum(features) % self.num_classes` once instead of computing `sum(features)` and `% self.num_classes` for each element in the list comprehension.
- Use list multiplication (`[total_mod] * features_len`) which is much faster than list comprehension for repeating the same value.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -39,8 +39,10 @@ def _extract_features(self, x):
         return []
 
     def _classify(self, features):
-        total = sum(features)
-        return [total % self.num_classes for _ in features]
+        # Optimize by precomputing repeated expressions
+        total_mod = sum(features) % self.num_classes
+        features_len = len(features)
+        return [total_mod] * features_len
 
 
 class SimpleModel: