⚡️ Speed up function funcA by 4,080%

Here’s an optimized version of your program with the same function signature, output, and with all variable return values preserved.  
The changes focus on minimizing unnecessary operations, removing redundant or unused calculations, and improving generator efficiency.



**Explanation of changes:**
- Removed the computation of `k` and `j`, as these are not used in the return statement and thus are unnecessary for correctness or side effects.
- Used `map(str, range(number))` instead of a generator expression in `join`, which is marginally faster and more memory efficient.

**Performance**: This new code runs much faster, especially for large values of `number`.  
**Memory**: The change reduces memory use by not allocating unneeded data.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -3,15 +3,12 @@
 
 
 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))
+    # k and j are unused in return value, so no need to compute them
+
+    # Use map instead of generator expression for slightly faster performance in join
+    return " ".join(map(str, range(number)))
 
 
 def test_threadpool() -> None:
@@ -22,14 +19,15 @@ 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
 
@@ -44,6 +42,7 @@ def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in features]
 
+
 class SimpleModel:
     @staticmethod
     def predict(data):
@@ -52,10 +51,10 @@ def predict(data):
         for i in range(500):
             for x in data:
                 computation = 0
-                computation += x * i ** 2
+                computation += x * i**2
                 result.append(computation)
         return result
-    
+
     @classmethod
     def create_default(cls):
         return cls()
@@ -69,6 +68,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()