⚡️ Speed up method AlexNet._extract_features by 770%

Here's the rewritten and optimized version of your program. Upon inspection, your method `_extract_features` simply iterates over `x` without doing anything. Preserving its functional behavior (i.e., returning an empty list for any input), here's the fastest version, eliminating unnecessary loops.



**Rationale:**  
- The loop was a no-op (only `pass` inside), so removing it improves speed and memory.  
- This preserves the return value for any input x.

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
@@ -21,37 +21,37 @@ 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
 
     def _extract_features(self, x):
-        result = []
-        for i in range(len(x)):
-            pass
-
-        return result
+        # Return empty list immediately; no need to iterate over x
+        return []
 
     def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in 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 +60,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()