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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 hyperbolic_errors(hyp_axes, xvals, transformation=None, axes=None, means=None, correct_apparent_dip=True, reverse=False):
 """ Returns a function that can be used to create a view of the hyperbolic error ellipse from a specific direction. This creates a hyperbolic quadric and slices it to form a conic on a 2d cartesian plane aligned with the requested direction. A function is returned that takes x values (distance along nominal line) and returns y values (width of error hyperbola) kwargs: transformation rotation to apply to quadric prior to slicing (e.g. transformation into 'world' coordinates axes axes on which to slice the data """

    if means is None:
        means = N.array([0,0])

    arr = augment_tensor(N.diag(hyp_axes))

    # Transform ellipsoid to dual hyperboloid
    hyp = conic(arr).dual()

    if len(hyp_axes) == 3:
        # Three_dimensional case

        if transformation is None:
            transformation = N.identity(3)
        if axes is None:
            axes = N.array([[0,1,0],[0,0,1]])

        hyp = hyp.transform(augment_tensor(transformation))

        n_ = N.cross(axes[0],axes[1])

        # Create a plane containing the two axes specified
        # in the function call
        p = plane(n_) # no offset (goes through origin)
        h1 = hyp.slice(p, axes=axes)[0]
    else:
        # We have a 2d geometry
        h1 = hyp

    # Major axes of the conic sliced in the requested viewing
    # geometry
    A = N.sqrt(h1.semiaxes())

    yvals = A[1]*N.cosh(N.arcsinh(xvals/A[0]))

    vals = N.array([xvals,yvals]).transpose()
    nom = N.array([xvals,N.zeros(xvals.shape)]).transpose()

    # Rotate the whole result if the PCA axes aren't aligned to the
    # major axes of the view coordinate system
    ax1 = apparent_dip_correction(axes)

    # This is a dirty hack to flip things left to right
    if reverse:
        ax1 = ax1.T
    # Top
    t = dot(vals,ax1).T+means[:,N.newaxis]
    # Btm
    vals[:,-1] *= -1
    b = dot(vals,ax1).T+means[:,N.newaxis]

    nom = dot(nom,ax1).T+means[:,N.newaxis]
    return nom, b, t[:,::-1]