<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
 """ Train a Gaussian Mixture clustering model. :param rdd: Training points as an `RDD` of `Vector` or convertible sequence types. :param k: Number of independent Gaussians in the mixture model. :param convergenceTol: Maximum change in log-likelihood at which convergence is considered to have occurred. (default: 1e-3) :param maxIterations: Maximum number of iterations allowed. (default: 100) :param seed: Random seed for initial Gaussian distribution. Set as None to generate seed based on system time. (default: None) :param initialModel: Initial GMM starting point, bypassing the random initialization. (default: None) """

        initialModelWeights = None
        initialModelMu = None
        initialModelSigma = None
        if initialModel is not None:
            if initialModel.k != k:
                raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
                                % (initialModel.k, k))
            initialModelWeights = list(initialModel.weights)
            initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
            initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
        java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
                                   k, convergenceTol, maxIterations, seed,
                                   initialModelWeights, initialModelMu, initialModelSigma)
        return GaussianMixtureModel(java_model)