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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 parallel_for(loop_function, parameters, nb_threads=100):
 """ Execute the loop body in parallel. .. note:: Race-Conditions Executing code in parallel can cause an error class called "race-condition". Parameters loop_function : Python function which takes a tuple as input parameters : List of tuples Each element here should be executed in parallel. Returns ------- return_values : list of return values """

    import multiprocessing.pool
    from contextlib import closing
    with closing(multiprocessing.pool.ThreadPool(nb_threads)) as pool:
        return pool.map(loop_function, parameters)