⚡️ Speed up method AlexNet._classify by 391%

Here is a faster version of your program.  
The main optimization is to hoist out `total % self.num_classes` so it is only computed once, then use `[val] * len(features)` to efficiently create the output list.  
This reduces `O(n)` mod operations to a single mod, and reduces Python's per-element loop overhead.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -2,7 +2,7 @@
 
 
 def funcA(number):
-    number = number if number < 1000 else 1000
+    number = min(1000, number)
     k = 0
     for i in range(number * 100):
         k += i
@@ -21,14 +21,15 @@ def test_threadpool() -> None:
     for r in result:
         print(r)
 
+
 class AlexNet:
     def __init__(self, num_classes=1000):
         self.num_classes = num_classes
         self.features_size = 256 * 6 * 6
 
     def forward(self, x):
         features = self._extract_features(x)
-        
+
         output = self._classify(features)
         return output
 
@@ -41,17 +42,20 @@ def _extract_features(self, x):
 
     def _classify(self, features):
         total = sum(features)
-        return [total % self.num_classes for _ in features]
+        result = total % self.num_classes
+        return [result] * len(features)
+
 
 class SimpleModel:
     @staticmethod
     def predict(data):
         return [x * 2 for x in data]
-    
+
     @classmethod
     def create_default(cls):
         return cls()
 
+
 def test_models():
     model = AlexNet(num_classes=10)
     input_data = [1, 2, 3, 4, 5]
@@ -60,6 +64,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()