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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def get_residual_norms(H, self_adjoint=False):
    '''Compute relative residual norms from Hessenberg matrix.

    It is assumed that the initial guess is chosen as zero.'''
    H = H.copy()
    n_, n = H.shape
    y = numpy.eye(n_, 1, dtype=H.dtype)
    resnorms = [1.]
    for i in range(n_-1):
        G = Givens(H[i:i+2, [i]])
        if self_adjoint:
            H[i:i+2, i:i+3] = G.apply(H[i:i+2, i:i+3])
        else:
            H[i:i+2, i:] = G.apply(H[i:i+2, i:])
        y[i:i+2] = G.apply(y[i:i+2])
        resnorms.append(numpy.abs(y[i+1, 0]))
    if n_ == n:
        resnorms.append(0.)
    return numpy.array(resnorms)