Implement a simple **edge-detection filter** for grayscale images by convolving the image with horizontal and vertical Sobel kernels and combining their responses into an edge-magnitude map.

The edge strength $G$ at each pixel is computed from the horizontal gradient $G_x$ and vertical gradient $G_y$ as:
$$
G = \sqrt{G_x^2 + G_y^2}
$$

## Requirements

Implement the function
```python
def sobel_edge_magnitude(image):
    \"""
    Computes Sobel edge magnitude for a grayscale image.

    Args:
        image (np.ndarray): Grayscale image of shape (H, W).

    Returns:
        np.ndarray: Edge magnitude map of shape (H, W).
    \"""
    # Your solution below
```

**Rules:**
- Use the Sobel kernels:
  - $K_x=\begin{bmatrix}-1&0&1\\-2&0&2\\-1&0&1\end{bmatrix}$, $K_y=\begin{bmatrix}-1&-2&-1\\0&0&0\\1&2&1\end{bmatrix}$
- Compute $G_x$ and $G_y$ by 2D convolution with **zero padding** so the output stays shape $(H, W)$.
- Compute edge magnitude per pixel as $G=\sqrt{G_x^2+G_y^2}$.
- Return the output as `np.ndarray` of float type.
- Use only **NumPy** (and Python built-ins); don’t use OpenCV/SciPy convolution helpers.

## Example

```python
image = np.array([
    [0., 0., 0.],
    [0., 255., 0.],
    [0., 0., 0.]
])

sobel_edge_magnitude(image)
```

Output:
```python
array([
    [360.6245, 510.0, 360.6245],
    [510.0, 0.0, 510.0],
    [360.6245, 510.0, 360.6245]
])
```

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Edge detection filters

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