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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, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2", intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
 """ Train a logistic regression model on the given data. :param data: The training data, an RDD of LabeledPoint. :param iterations: The number of iterations. (default: 100) :param initialWeights: The initial weights. (default: None) :param regParam: The regularizer parameter. (default: 0.0) :param regType: The type of regularizer used for training our model. Supported values: - "l1" for using L1 regularization - "l2" for using L2 regularization (default) - None for no regularization :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e., whether bias features are activated or not). (default: False) :param corrections: The number of corrections used in the LBFGS update. If a known updater is used for binary classification, it calls the ml implementation and this parameter will have no effect. (default: 10) :param tolerance: The convergence tolerance of iterations for L-BFGS. (default: 1e-6) :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. (default: True) :param numClasses: The number of classes (i.e., outcomes) a label can take in Multinomial Logistic Regression. (default: 2) 1 0 """

        def train(rdd, i):
            return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
                                 float(regParam), regType, bool(intercept), int(corrections),
                                 float(tolerance), bool(validateData), int(numClasses))

        if initialWeights is None:
            if numClasses == 2:
                initialWeights = [0.0] * len(data.first().features)
            else:
                if intercept:
                    initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
                else:
                    initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
        return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)