⚡️ Speed up method AlexNet._classify by 379%

Here is an optimized version of your program.  
Improvements focus on avoiding redundant computation in the list comprehension by pre-computing the result that does not depend on the loop variable.



**Summary of optimizations:**
- Compute `mod_val` only once instead of in every loop iteration.
- Use `[mod_val] * len(features)` to quickly create the result list.
- Handle empty `features` efficiently.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -41,8 +41,12 @@ def _extract_features(self, x):
         return result
 
     def _classify(self, features):
-        total = sum(features)
-        return [total % self.num_classes for _ in features]
+        # Compute total sum once, then compute modulo only once.
+        # Use list multiplication for fast vector creation.
+        if not features:
+            return []
+        mod_val = sum(features) % self.num_classes
+        return [mod_val] * len(features)
 
 
 class SimpleModel: