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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 apparent_dip_correction(axes):
 """ Produces a two-dimensional rotation matrix that rotates a projected dataset to correct for apparent dip """

    a1 = axes[0].copy()
    a1[-1] = 0
    cosa = angle(axes[0],a1,cos=True)
    _ = 1-cosa**2
    if _ > 1e-12:
        sina = N.sqrt(_)
        if cosa < 0:
            sina *= -1
        # Construct rotation matrix
        R= N.array([[cosa,sina],[-sina,cosa]])
    else:
        # Small angle, don't bother
        # (small angles can lead to spurious results)
        R = N.identity(2)
    #if axes[0,0] < 0:
    #    return R.T
    #else:
    return R