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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 train(self, rdd, k=4, maxIterations=20, minDivisibleClusterSize=1.0, seed=-1888008604):
 """ Runs the bisecting k-means algorithm return the model. :param rdd: Training points as an `RDD` of `Vector` or convertible sequence types. :param k: The desired number of leaf clusters. The actual number could be smaller if there are no divisible leaf clusters. (default: 4) :param maxIterations: Maximum number of iterations allowed to split clusters. (default: 20) :param minDivisibleClusterSize: Minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster. (default: 1) :param seed: Random seed value for cluster initialization. (default: -1888008604 from classOf[BisectingKMeans].getName.##) """

        java_model = callMLlibFunc(
            "trainBisectingKMeans", rdd.map(_convert_to_vector),
            k, maxIterations, minDivisibleClusterSize, seed)
        return BisectingKMeansModel(java_model)