⚡️ Speed up method AlexNet._classify by 384%

Here is an optimized version of your program. The major performance gain comes from using more efficient built-in functions and avoiding unnecessary computation inside the loop.  
Key points.
- Instead of creating a list using a list comprehension, use list multiplication, as `total % self.num_classes` is a constant for all elements.
- `sum(features)` is already efficient, but now it is only called once, and the modulo operation is also computed just once.

Optimized code.


This eliminates redundant computation inside the loop, resulting in a much faster classification, especially for large feature vectors.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -39,8 +39,9 @@ def _extract_features(self, x):
         return result
 
     def _classify(self, features):
-        total = sum(features)
-        return [total % self.num_classes for _ in features]
+        # Compute the sum and modulo just once, then construct the result list efficiently
+        mod_val = sum(features) % self.num_classes
+        return [mod_val] * len(features)
 
 
 class SimpleModel: