Compute **information gain** for a candidate split in a decision tree, which tells you how much uncertainty about the labels is reduced after splitting. You’ll implement entropy and use it to score a split given arrays of labels and feature values.

The Information Gain for a binary split is defined as:
$$
IG(Y) = H(Y) - \left( \frac{n_{left}}{n} H(Y_{left}) + \frac{n_{right}}{n} H(Y_{right}) \right)
$$
where $H(Y)$ is the entropy of the labels.

## Requirements

Implement the function
```python
def information_gain(y, x, threshold):
    \"""
    Computes information gain for a binary split x <= threshold vs x > threshold.

    Args:
        y (np.ndarray): Class labels (e.g., 0/1 or 0..K-1), length n.
        x (np.ndarray): Feature values aligned with y, length n.
        threshold (float): Split threshold.

    Returns:
        float: Information gain IG = H(y) - [ (n_L/n) * H(y_L) + (n_R/n) * H(y_R) ].
    \"""
    # Your solution below
```

**Rules:**
- Use entropy $H(S) = -\sum_{c} p(c)\log_2 p(c)$ (ignore terms where $p(c)=0$).
- Split into left group $L=\{i: x_i \le t\}$ and right group $R=\{i: x_i > t\}$.
- Use only **NumPy**.
- Return a single float.

## Example

```python
y = np.array([0, 0, 1, 1])
x = np.array([2.0, 3.0, 8.0, 9.0])
threshold = 5.0

information_gain(y, x, threshold)
```

Output:
```python
1.0
```

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Information gain

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Argument	Type
x	np.ndarray
y	np.ndarray
threshold	float