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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 standard_deviation_difference(data, ground_truth, mask=None, normalized=False, force_lower_is_better=True):
 r"""Return absolute diff in std between ``data`` and ``ground_truth``. Parameters data : `Tensor` or `array-like` Input data to compare to the ground truth. If not a `Tensor`, an unweighted tensor space will be assumed. ground_truth : `array-like` Reference to which ``data`` should be compared. mask : `array-like`, optional If given, ``data * mask`` is compared to ``ground_truth * mask``. normalized : bool, optional Boolean flag to switch between unormalized and normalized FOM. force_lower_is_better : bool, optional If ``True``, it is ensured that lower values correspond to better matches. For the standard deviation difference, this is already the case, and the flag is only present for compatibility to other figures of merit. Returns ------- sdd : float FOM value, where a lower value means a better match. Notes ----- The FOM evaluates .. math:: \mathrm{SDD}(f, g) = \Big| \| f - \overline{f} \|_2 - \| g - \overline{g} \|_2 \Big|, or, in normalized form .. math:: \mathrm{SDD_N}(f, g) = \frac{\Big| \| f - \overline{f} \|_2 - \| g - \overline{g} \|_2 \Big|} {\| f - \overline{f} \|_2 + \| g - \overline{g} \|_2}, where :math:`\overline{f}` is the mean value of :math:`f`, .. math:: \overline{f} = \frac{\langle f, 1\rangle}{\|1|_1}. The normalized variant takes values in :math:`[0, 1]`. """

    if not hasattr(data, 'space'):
        data = odl.vector(data)

    space = data.space
    ground_truth = space.element(ground_truth)

    l1_norm = odl.solvers.L1Norm(space)
    l2_norm = odl.solvers.L2Norm(space)

    if mask is not None:
        data = data * mask
        ground_truth = ground_truth * mask

    # Volume of space
    vol = l1_norm(space.one())

    data_mean = data.inner(space.one()) / vol
    ground_truth_mean = ground_truth.inner(space.one()) / vol

    deviation_data = l2_norm(data - data_mean)
    deviation_ground_truth = l2_norm(ground_truth - ground_truth_mean)
    fom = np.abs(deviation_data - deviation_ground_truth)

    if normalized:
        denom = deviation_data + deviation_ground_truth
        if denom == 0:
            fom = 0.0
        else:
            fom /= denom

    return fom