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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 trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy="auto", impurity="variance", maxDepth=4, maxBins=32, seed=None):
 """ Train a random forest model for regression. :param data: Training dataset: RDD of LabeledPoint. Labels are real numbers. :param categoricalFeaturesInfo: Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories :param numTrees: Number of trees in the random forest. :param featureSubsetStrategy: Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "onethird" for regression. (default: "auto") :param impurity: Criterion used for information gain calculation. The only supported value for regression is "variance". (default: "variance") :param maxDepth: Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 4) :param maxBins: Maximum number of bins used for splitting features. (default: 32) :param seed: Random seed for bootstrapping and choosing feature subsets. Set as None to generate seed based on system time. (default: None) :return: RandomForestModel that can be used for prediction. Example usage: 2 4 1.0 0.5 [1.0, 0.5] """

        return cls._train(data, "regression", 0, categoricalFeaturesInfo, numTrees,
                          featureSubsetStrategy, impurity, maxDepth, maxBins, seed)