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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 meta_model_fit(X_train, y_train, svm_hardness, fit_intercept, number_of_threads, regressor_type="LinearSVR"):
 """ Trains meta-labeler for predicting number of labels for each user. Based on: Tang, L., Rajan, S., & Narayanan, V. K. (2009, April). Large scale multi-label classification via metalabeler. In Proceedings of the 18th international conference on World wide web (pp. 211-220). ACM. """

    if regressor_type == "LinearSVR":
        if X_train.shape[0] > X_train.shape[1]:
            dual = False
        else:
            dual = True

        model = LinearSVR(C=svm_hardness, random_state=0, dual=dual,
                          fit_intercept=fit_intercept)
        y_train_meta = y_train.sum(axis=1)
        model.fit(X_train, y_train_meta)
    else:
        print("Invalid regressor type.")
        raise RuntimeError

    return model