⚡️ Speed up method AlexNet._classify by 359%

Here is an optimized version of your code. The main bottleneck is the list comprehension, which recalculates `total % self.num_classes` for every element in `features`, even though this value never changes within a single call. By computing it once and multiplying it with `[1]*len(features)` (to create the repeated list quickly), we save significant computation time. Also, using `len(features)` instead of iterating over `features` is slightly faster for large lists.

Here's the rewritten code.



**Changes made:**
- Compute `total % self.num_classes` only once and store in `mod_val`.
- Replace the list comprehension with a single multiplication: `[mod_val] * len(features)`.

This avoids both redundant modulo operations and Python's slower list comprehension for repeating a single value. The result and output remain exactly the same. The function is now allocation and compute efficient.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -42,7 +42,8 @@ def _extract_features(self, x):
 
     def _classify(self, features):
         total = sum(features)
-        return [total % self.num_classes for _ in features]
+        mod_val = total % self.num_classes
+        return [mod_val] * len(features)
 
 
 class SimpleModel: