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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 removeOutliers(points, radius):
 """ Remove outliers from a cloud of points within the specified `radius` search. .. hint:: |clustering| |clustering.py|_ """

    isactor = False
    if isinstance(points, vtk.vtkActor):
        isactor = True
        poly = points.GetMapper().GetInput()
    else:
        src = vtk.vtkPointSource()
        src.SetNumberOfPoints(len(points))
        src.Update()
        vpts = src.GetOutput().GetPoints()
        for i, p in enumerate(points):
            vpts.SetPoint(i, p)
        poly = src.GetOutput()

    removal = vtk.vtkRadiusOutlierRemoval()
    removal.SetInputData(poly)
    removal.SetRadius(radius)
    removal.SetNumberOfNeighbors(5)
    removal.GenerateOutliersOff()
    removal.Update()
    rpoly = removal.GetOutput()
    print("# of removed outlier points: ",
          removal.GetNumberOfPointsRemoved(), '/', poly.GetNumberOfPoints())
    outpts = []
    for i in range(rpoly.GetNumberOfPoints()):
        outpts.append(list(rpoly.GetPoint(i)))
    outpts = np.array(outpts)
    if not isactor:
        return outpts

    actor = vs.Points(outpts)
    return actor