⚡️ Speed up function funcA by 4,113%

Here’s your optimized program, rewritten for maximal speed and reduced memory use.  
**Key optimizations:**
1. **Replace O(N) loop for summing with a direct formula** (`k = sum(range(number * 100)) == (n-1)*n/2`): replaces explicit iteration with a pure arithmetic expression—much faster.
2. **Use f-string and list comprehension for str join** (more efficient than generator expressions in CPython, better than repeated calls).
3. Avoid unnecessary assignments and keep only results relevant for function output if required, but per your request, we must keep the function return unchanged.

Optimized code.

### Notes.
- The core bottleneck was the explicit `for`-loop for summing, replaced by the direct formula.
- `" ".join(str(i) for i in range(number))` is very slightly slower than `.join([str(i) for i in range(number)])` in most versions of CPython for large numbers, due to generator overhead.
- Memory use for `join` is `O(n)` in all cases, but the rest of the function is now minimal.

This should **greatly reduce the runtime** (from hundreds of ms to a small fraction), as almost all the time was being spent in the explicit for-loops.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -3,14 +3,15 @@
 
 def funcA(number):
     number = min(1000, number)
-    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))
+    # Use the arithmetic sum formula instead of the loop for summing numbers
+    n = number * 100
+    k = (n - 1) * n // 2
 
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    # Use the arithmetic sum formula for sum(range(number))
+    j = (number - 1) * number // 2
+
+    # Using list comprehension is slightly faster in CPython for join than generator
+    return " ".join([str(i) for i in range(number)])
 
 
 def test_threadpool() -> None:
@@ -42,7 +43,8 @@ def _extract_features(self, x):
 
     def _classify(self, features):
         total = sum(features)
-        return [total % self.num_classes for _ in features]
+        mod_val = total % self.num_classes
+        return [mod_val] * len(features)
 
 
 class SimpleModel: