Eigenvectors and eigenspaces show up all over ML (PCA, stability, dynamical systems) and are core linear algebra skills. In this task, you’ll compute the eigenspace dimension(s) for real eigenvalues of a small matrix using NumPy.

An eigenvalue $\lambda$ and eigenvector $\mathbf{v}$ satisfy $A\mathbf{v} = \lambda\mathbf{v}$. The **eigenspace** corresponding to $\lambda$ is the set of all such vectors. The dimension of this space (geometric multiplicity) is given by:
$$
\dim(\mathcal{E}_\lambda) = n - \operatorname{rank}(A - \lambda I)
$$

## Requirements

Implement the function
```python
def eigenspace_dimensions(A, tol=1e-8):
    \"""
    Computes the geometric multiplicity (eigenspace dimension) for each real eigenvalue of A.

    Args:
        A (np.ndarray): Square matrix of shape (n, n).
        tol (float): Tolerance for deciding whether an eigenvalue is real and for rank decisions.

    Returns:
        list[tuple[float, int]]: A list of (eigenvalue, dimension) pairs for each distinct real eigenvalue,
        sorted by eigenvalue ascending.
    \"""
    # Your solution below
```

**Rules:**
- Use NumPy to compute eigenvalues, and group eigenvalues that are equal within `tol` into a single “distinct” eigenvalue.
- For each distinct real eigenvalue $ \lambda $, compute the eigenspace dimension as the nullity of $A - \lambda I$, using:
  $$
  \dim(\mathcal{E}_\lambda) = n - \operatorname{rank}(A - \lambda I)
  $$
- Treat an eigenvalue as real if `abs(imag_part) <= tol`.
- Return only real eigenvalues (ignore complex ones).
- Don’t use any symbolic math libraries; stick to NumPy + built-in Python.

## Example

```python
A = np.array([
    [2.0, 0.0],
    [0.0, 3.0]
])

eigenspace_dimensions(A)
```

Output:
```python
[(2.0, 1), (3.0, 1)]
```

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Eigenvectors and eigenspaces

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1414. Eigenvectors and eigenspaces

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Argument	Type
A	np.ndarray
tol	float