Optimize sorter

Impact: high
 Impact_explanation: Looking at this optimization pull request, I need to assess several key factors:

## Performance Analysis
The speedup is absolutely massive - **172,663% improvement** on average. The test results show consistent and dramatic improvements across all scenarios:
- Small arrays: 32-170% faster
- Large arrays: 9,453-113,181% faster 
- Existing tests show speedups ranging from 190% to 588,974%

## Code Quality Assessment
**Positive aspects:**
- Replaces inefficient O(n²) bubble sort with Python's highly optimized Timsort (O(n log n))
- Uses built-in `arr.sort()` which is implemented in C for maximum performance
- Maintains identical functionality and API
- Code becomes much cleaner and more maintainable
- Follows Python best practices (use built-ins when available)

**Concerns:**
- This is essentially changing the algorithm entirely, not just optimizing the existing implementation
- The original bubble sort implementation might have been intentional for educational purposes or specific requirements

## Hot Path Analysis
From the calling function details, I can see:
- The function is called from multiple test files with large datasets (5000 elements)
- It's used in computational workflows (`compute_and_sort`)
- Multiple integration points suggest this is a core utility function

## Technical Correctness
- All tests pass with 100% coverage
- The optimization maintains the same behavior (in-place sorting, return value)
- Handles all edge cases correctly (empty lists, single elements, mixed types, etc.)
- TypeError handling is preserved for incomparable types

## Trade-off Assessment
**Benefits:**
- Massive performance improvement
- Cleaner, more maintainable code
- Leverages decades of sorting algorithm research
- Better algorithmic complexity

**Potential downsides:**
- Changes the fundamental algorithm (though this is generally positive)
- If the original bubble sort was needed for educational/demonstration purposes, this removes that

## Final Assessment
This is an exceptional optimization that provides massive performance gains while improving code quality. The fact that it maintains identical functionality while being dramatically faster makes this a clear win. The performance improvements are so substantial (orders of magnitude) that they would be beneficial in virtually any context.

The only scenario where I might hesitate is if this was specifically educational code meant to demonstrate bubble sort algorithm, but even then, the performance benefits are so significant that it would be worth discussing with the developer.

 END OF IMPACT EXPLANATION
CALLING CONTEXT 
```python:code_to_optimize/bubble_sort_from_another_file.py
def sort_from_another_file(arr):
    sorted_arr = sorter(arr)
    return sorted_arr
```
```python:code_to_optimize/code_directories/my-best-repo/tests/test_full_bubble_coverage.py
def test_sort():
    input = [5, 4, 3, 2, 1, 0]
    output = sorter(input)
    assert output == [0, 1, 2, 3, 4, 5]

    input = [5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
    output = sorter(input)
    assert output == [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]

    input = list(reversed(range(5000)))
    output = sorter(input)
    assert output == list(range(5000))
```
```python:code_to_optimize/process_and_bubble_sort.py
def compute_and_sort(arr):
    # Compute pairwise sums average
    pairwise_average = calculate_pairwise_products(arr)

    # Call sorter function
    sorter(arr.copy())

    return pairwise_average
```
```python:code_to_optimize/process_and_bubble_sort_codeflash_trace.py
def compute_and_sort(arr):
    # Compute pairwise sums average
    pairwise_average = calculate_pairwise_products(arr)

    # Call sorter function
    sorter(arr.copy())

    return pairwise_average
```
```python:code_to_optimize/tests/unittest/test_bubble_sort.py
    def test_sort(self):
        input = [5, 4, 3, 2, 1, 0]
        output = sorter(input)
        self.assertEqual(output, [0, 1, 2, 3, 4, 5])

        input = [5.0, 4.0, 3.0, 2.0, 1.0, 0.0]
        output = sorter(input)
        self.assertEqual(output, [0.0, 1.0, 2.0, 3.0, 4.0, 5.0])

        input = list(reversed(range(5000)))
        output = sorter(input)
        self.assertEqual(output, list(range(5000)))
```

END OF CALLING CONTEXT

The optimized code replaces the manual bubble sort implementation with Python's built-in `arr.sort()` method, resulting in a dramatic **172,663% speedup**.

**Key optimization:**
- **Algorithm change**: Replaced O(n²) bubble sort with Python's highly optimized Timsort algorithm (O(n log n) average case, O(n) best case)
- **Implementation efficiency**: Python's built-in sort is implemented in C and uses advanced optimizations like adaptive merging, binary insertion sort for small arrays, and galloping mode

**Why this leads to massive speedup:**
1. **Algorithmic complexity**: Bubble sort performs O(n²) comparisons and swaps, while Timsort performs O(n log n) operations on average
2. **C-level implementation**: Built-in sort runs at native C speed rather than Python bytecode interpretation 
3. **Adaptive optimizations**: Timsort recognizes and exploits existing order in data, making it extremely fast for already-sorted or partially-sorted inputs

**Performance characteristics by test case:**
- **Small arrays (≤10 elements)**: 32-170% faster due to reduced Python overhead
- **Large arrays (1000 elements)**: 9,453-113,181% faster, with the largest gains on reverse-sorted lists where bubble sort performs worst
- **Already sorted data**: Exceptional performance due to Timsort's O(n) best-case behavior
- **All data types**: Consistent improvements across integers, floats, strings, and custom objects

The optimization maintains identical functionality while leveraging decades of sorting algorithm research and implementation optimization.

Author: codeflash-ai[bot]

code_to_optimize/bubble_sort.py

@@ -1,10 +1,5 @@
 def sorter(arr):
     print("codeflash stdout: Sorting list")
-    for i in range(len(arr)):
-        for j in range(len(arr) - 1):
-            if arr[j] > arr[j + 1]:
-                temp = arr[j]
-                arr[j] = arr[j + 1]
-                arr[j + 1] = temp
+    arr.sort()
     print(f"result: {arr}")
     return arr