⚡️ Speed up method AlexNet._classify by 333%

Here is an optimized version of your program. The primary optimization opportunity is replacing the use of `sum(features)` and the list comprehension `[total % self.num_classes for _ in features]` with a more efficient approach.  
Since the total and num_classes never change inside the method, we compute the modulo only once, then reuse it for all outputs with multiplication rather than generating a new list with a comprehension.



**Explanation of optimizations:**
- **sum(features) % self.num_classes** is computed once instead of computing `total % self.num_classes` for every element of `features`.
- **List multiplication** (`[total_mod] * len(features)`) is faster than a list comprehension over the same value, as it doesn't loop nor call the modulo operator repeatedly.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -38,8 +38,8 @@ def _extract_features(self, x):
         return result
 
     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)  # Return same value for all as total_mod
 
 
 class SimpleModel: