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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 _generate_base_svm_regression_spec(model):
 """ Takes an SVM regression model produces a starting spec using the parts. that are shared between all SVMs. """

    if not(_HAS_SKLEARN):
        raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')

    spec = _Model_pb2.Model()
    spec.specificationVersion = SPECIFICATION_VERSION
    svm = spec.supportVectorRegressor

    _set_kernel(model, svm)

    svm.rho = -model.intercept_[0]
    for i in range(len(model._dual_coef_)):
        for cur_alpha in model._dual_coef_[i]:
            svm.coefficients.alpha.append(cur_alpha)

    for cur_src_vector in model.support_vectors_:
        cur_dest_vector = svm.denseSupportVectors.vectors.add()
        for i in cur_src_vector:
            cur_dest_vector.values.append(i)
    return spec