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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 false_structures_mask(foreground, smoothness_factor=None):
 """Return mask emphasizing areas outside ``foreground``. Parameters foreground : `Tensor` or `array-like` The region that should be de-emphasized. If not a `Tensor`, an unweighted tensor space will be assumed. ground_truth : `array-like` Reference to which ``data`` should be compared. foreground : `FnBaseVector` The region that should be de-emphasized. smoothness_factor : float, optional Positive real number. Higher value gives smoother transition between foreground and its complement. Returns ------- result : `Tensor` or `numpy.ndarray` Euclidean distances of elements in ``foreground``. The return value is a `Tensor` if ``foreground`` is one, too, otherwise a NumPy array. Examples -------- array([ 0.4, 0.2, 0. , 0.2, 0.4]) Raises ------ ValueError If foreground is all zero or all one, or contains values not in {0, 1}. Notes ----- This helper function computes the Euclidean distance transform from each point in ``foreground.space`` to ``foreground``. The weighting gives higher values to structures outside the foreground as defined by the mask. """

    try:
        space = foreground.space
        has_space = True
    except AttributeError:
        has_space = False
        foreground = np.asarray(foreground)
        space = odl.tensor_space(foreground.shape, foreground.dtype)
        foreground = space.element(foreground)

    from scipy.ndimage.morphology import distance_transform_edt

    unique = np.unique(foreground)
    if not np.array_equiv(unique, [0., 1.]):
        raise ValueError('`foreground` is not a binary mask or has '
                         'either only true or only false values {!r}'
                         ''.format(unique))

    result = distance_transform_edt(
        1.0 - foreground, sampling=getattr(space, 'cell_sides', 1.0)
    )
    if has_space:
        return space.element(result)
    else:
        return result