[Python] How to find the general inverse of a matrix in python using SVD?

So I am trying to find the generalised inverse A+ of a matrix A using svd

this is my code for svd

A4 = np.array([[3, 1,2,3,4],
[5,2,3,4,5],
[7,4,5,6,7]])
print(A4)
U4, s4, V4 = np.linalg.svd(A4)
D4 = sp.linalg.diagsvd(s4, 3, 5)

print('UDVt =\n', U4@D4@V4 )


Which works fine and gives me the correct matrix

And this is the code that I am trying to use to get the generalised inverse

D4p = np.array([[D4[0,0]**-1,0,0],
[0,D4[1,1]**-1,0],
[0,0,D4[2,2]**-1],
[0,0,0],
[0,0,0]])
A4plus = np.transpose(V4) @ D4p @ np.transpose(U4)
print(np.round(A4plus @ A4,2))
print(np.round(A4 @ A4plus,2))


I have tried changing the transposed matrixed.

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