⚡️ Speed up method AlexNet._classify by 340%

Here is an optimized version of your AlexNet class. Your original code recalculates `total % self.num_classes` for every item in `features`, but this value does not change within the list comprehension. We can compute it once and repeat it, making the code faster and also a bit more memory efficient.



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This version calculates `sum(features) % self.num_classes` only once and multiplies into a list, which is faster than using a list comprehension that repeats the calculation.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -38,8 +38,9 @@ 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
+        # Compute total_mod once and repeat it for the length of features
+        return [total_mod] * len(features)
 
 
 class SimpleModel: