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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(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", seed=None, initializationSteps=2, epsilon=1e-4, initialModel=None):
 """ Train a k-means clustering model. :param rdd: Training points as an `RDD` of `Vector` or convertible sequence types. :param k: Number of clusters to create. :param maxIterations: Maximum number of iterations allowed. (default: 100) :param runs: This param has no effect since Spark 2.0.0. :param initializationMode: The initialization algorithm. This can be either "random" or "k-means||". (default: "k-means||") :param seed: Random seed value for cluster initialization. Set as None to generate seed based on system time. (default: None) :param initializationSteps: Number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. (default: 2) :param epsilon: Distance threshold within which a center will be considered to have converged. If all centers move less than this Euclidean distance, iterations are stopped. (default: 1e-4) :param initialModel: Initial cluster centers can be provided as a KMeansModel object rather than using the random or k-means|| initializationModel. (default: None) """

        if runs != 1:
            warnings.warn("The param `runs` has no effect since Spark 2.0.0.")
        clusterInitialModel = []
        if initialModel is not None:
            if not isinstance(initialModel, KMeansModel):
                raise Exception("initialModel is of "+str(type(initialModel))+". It needs "
                                "to be of <type 'KMeansModel'>")
            clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters]
        model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
                              runs, initializationMode, seed, initializationSteps, epsilon,
                              clusterInitialModel)
        centers = callJavaFunc(rdd.context, model.clusterCenters)
        return KMeansModel([c.toArray() for c in centers])