You’ll practice core determinant properties that show up a lot in linear algebra interviews, like how row operations affect the determinant. Given a square matrix as a NumPy array, compute its determinant using only simple row operations and a recursive base case.

$$
\det(A) = (-1)^k \prod_{i=1}^n U_{ii}
$$

## Requirements

Implement the function
```python
import numpy as np

def determinant_via_row_ops(A):
    \"""
    Computes the determinant of a square matrix using row operations.

    Args:
        A (np.ndarray): Square matrix of shape (n, n).

    Returns:
        float: The determinant of A.

    Notes:
        You should use determinant properties:
        - Swapping two rows flips the sign of the determinant.
        - Scaling a row by c scales the determinant by c.
        - Adding a multiple of one row to another does not change the determinant.
    \"""
    # Your solution below
```

**Rules:**
- Use the determinant identity $$\det(A)=\prod_{i=1}^{n} u_{ii} \cdot s$$ where $u_{ii}$ are diagonal entries after converting $A$ to an upper-triangular matrix via row operations, and $s\in\{+1,-1\}$ tracks row swaps.
- Convert `A` to an upper-triangular form using only row swaps and “add multiple of row” operations (Gaussian elimination style).
- Track row swaps to adjust the sign of the final determinant.
- Do not call `np.linalg.det` (or any other prebuilt determinant routine).
- Return a single float.

## Example

```python
A = np.array([
    [2.0, 1.0],
    [5.0, 3.0]
])

determinant_via_row_ops(A)
```

Output:
```python
1.0
```

3Sum

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Determinant properties

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174. Determinant properties

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