⚡️ Speed up function mysorter by 88,562%

The optimization replaces the inefficient O(n²) bubble sort algorithm with Python's built-in `arr.sort()` method, which uses Timsort - a highly optimized O(n log n) sorting algorithm.

**Key changes:**
- Removed the nested loops that performed bubble sort comparisons and swaps
- Replaced with a single `arr.sort()` call that sorts the array in-place
- Preserved all print statements and function behavior

**Why this is faster:**
The original bubble sort performs up to n² comparisons and swaps, making it extremely slow for larger datasets. The profiler shows 30+ million operations in the inner loops. Python's Timsort is specifically designed to be fast on real-world data patterns - it's adaptive (faster on partially sorted data), stable, and uses sophisticated techniques like galloping mode and merge optimization.

**Performance characteristics from tests:**
- **Small arrays (< 10 elements):** 15-60% speedup due to eliminating loop overhead
- **Medium arrays (100 elements):** 2,000-4,000% speedup as algorithmic complexity advantage emerges  
- **Large arrays (1000+ elements):** 40,000-97,000% speedup where the O(n log n) vs O(n²) difference becomes massive
- **Best case scenarios:** Already sorted or nearly sorted arrays show the highest speedups (up to 97,000%) because Timsort is adaptive and recognizes existing order

The 88,561% overall speedup demonstrates the dramatic difference between a naive O(n²) algorithm and Python's production-quality sorting implementation.

Author: codeflash-ai[bot]

codeflash/bubble_sort.py

@@ -0,0 +1,8 @@
+def mysorter(arr):
+    print("codeflash stdout: Sorting list")
+    arr.sort()  # Built-in in-place Timsort, O(n log n)
+    print(f"result: {arr}")
+    return arr
+
+
+mysorter([5, 4, 3, 2, 1])