Computing eigendecompositions using SymPy:
>>> from sympy import *
>>> a, b = symbols('a b')
>>> M = Matrix([[0,a],[0,b]])
>>> (M.T * M).eigenvects()
[(0, 1, [Matrix([
[1],
[0]])]), (a**2 + b**2, 1, [Matrix([
[0],
[1]])])]
>>> (M * M.T).eigenvects()
[(0, 1, [Matrix([
[-b/a],
[ 1]])]), (a**2 + b**2, 1, [Matrix([
[a/b],
[ 1]])])]
We now build the matrices in the SVD:
>>> U = (1/sqrt(a**2 + b**2)) * Matrix([[a,-b],[b,a]])
>>> S = diag(sqrt(a**2 + b**2),0)
>>> V = Matrix([[0,1],[1,0]])
>>> U * S * V.T
Matrix([
[0, a],
[0, b]])
The SVD is
$$\begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix} = \begin{bmatrix} \frac{a}{\sqrt{a^{2} + b^{2}}} & - \frac{b}{\sqrt{a^{2} + b^{2}}}\\ \frac{b}{\sqrt{a^{2} + b^{2}}} & \frac{a}{\sqrt{a^{2} + b^{2}}} \end{bmatrix} \begin{bmatrix} \sqrt{a^{2} + b^{2}} & 0\\ 0 & 0\end{bmatrix} \begin{bmatrix} 0 & 1\\ 1 & 0\end{bmatrix}^{\top}$$
Hence, the pseudoinverse is
$$\begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix}^{+} = \begin{bmatrix} 0 & 1\\ 1 & 0\end{bmatrix} \begin{bmatrix} \frac{1}{\sqrt{a^{2} + b^{2}}} & 0\\ 0 & 0\end{bmatrix} \begin{bmatrix} \frac{a}{\sqrt{a^{2} + b^{2}}} & - \frac{b}{\sqrt{a^{2} + b^{2}}}\\ \frac{b}{\sqrt{a^{2} + b^{2}}} & \frac{a}{\sqrt{a^{2} + b^{2}}} \end{bmatrix}^{\top} = \color{blue}{\begin{bmatrix} 0 & 0\\ \frac{a}{a^{2} + b^{2}} & \frac{b}{a^{2} + b^{2}}\end{bmatrix}}$$
Verifying,
$$\begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix} \begin{bmatrix} 0 & 0\\ \frac{a}{a^{2} + b^{2}} & \frac{b}{a^{2} + b^{2}}\end{bmatrix} \begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix} = \begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix}$$
$$\begin{bmatrix} 0 & 0\\ \frac{a}{a^{2} + b^{2}} & \frac{b}{a^{2} + b^{2}}\end{bmatrix} \begin{bmatrix} 0 & a\\ 0 & b\end{bmatrix} \begin{bmatrix} 0 & 0\\ \frac{a}{a^{2} + b^{2}} & \frac{b}{a^{2} + b^{2}}\end{bmatrix} = \begin{bmatrix} 0 & 0\\ \frac{a}{a^{2} + b^{2}} & \frac{b}{a^{2} + b^{2}}\end{bmatrix}$$
Also, both products of the given matrix and its pseudoinverse are symmetric, as required.
This is the real case. The complex case should be easy to tackle.