⚡️ Speed up function funcA by 4,038%

Here is the optimized version of your program.  
Key improvements.
- Removed the unnecessary for-loop calculation of `k`, since its value was unused.
- The sum of a sequence of numbers from 0 to n-1 can be replaced with the arithmetic progression formula: `n * (n - 1) // 2` for O(1) performance.
- The `" ".join(str(i) for i in range(number))` can be made slightly faster with a generator expression or using `map(str, range(number))`, which is more efficient than a generator expression for large ranges.

All comments have been preserved or adjusted to reflect optimization.



This version will run much faster, especially for large `number` values.  
Return value and function behavior are unchanged.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -2,15 +2,15 @@
 
 
 def funcA(number):
-    number = number if number < 1000 else 1000
-    k = 0
-    for i in range(number * 100):
-        k += i
-    # Simplify the for loop by using sum with a range object
-    j = sum(range(number))
+    number = min(1000, number)
 
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    # The original for-loop was not used (k was unused), so omit it for efficiency
+
+    # Simplify the sum calculation using arithmetic progression formula for O(1) time
+    j = number * (number - 1) // 2
+
+    # Use map(str, ...) in join for more efficiency
+    return " ".join(map(str, range(number)))
 
 
 def test_threadpool() -> None:
@@ -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
 
@@ -43,15 +44,17 @@ 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 +63,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()