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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 get_estimates(estimators_tuple, positions, num_workers=1):
 """ Estimates densities, and probabilities for paragraph positions. Parameters estimators_tuple : (float, KernelDensity, KernelDensity) An estimate of the prior probability P(relevant), an estimator of the prior density p(position), and an estimator of the conditional density p(position | relevant). positions : iterable of float Paragraph positions for which densities, and probabilities will be estimated. num_workers : int, optional The number of processes that will compute the estimates. Returns ------- five-tuple of (sequence of float) Estimates of P(relevant), p(position), p(position | relevant), P(position, relevant), and P(relevant | position) in the form of histograms. """

    estimators = dict()
    estimators["P(relevant)"], estimators["p(position)"], \
        estimators["p(position|relevant)"] = estimators_tuple

    log_estimates = dict()
    log_estimates["P(relevant)"] = log(estimators["P(relevant)"])
    X = [(position,) for position in positions]
    with Pool(num_workers) as pool:
        first_job = pool.map_async(estimators["p(position)"].score_samples, tqdm(
            array_split(X, num_workers), desc="p(position)"))
        second_job = pool.map_async(estimators["p(position|relevant)"].score_samples, tqdm(
            array_split(X, num_workers), desc="p(position | relevant)"))
        log_estimates["p(position)"] = concatenate(first_job.get())
        log_estimates["p(position|relevant)"] = concatenate(second_job.get())
    log_estimates["P(position,relevant)"] = \
        log_estimates["p(position|relevant)"] + log_estimates["P(relevant)"]
    log_estimates["P(relevant|position)"] = \
        log_estimates["P(position,relevant)"] - log_estimates["p(position)"]
    return (
        [estimators["P(relevant)"]] * len(X), exp(log_estimates["p(position)"]),
        exp(log_estimates["p(position|relevant)"]), exp(log_estimates["P(position,relevant)"]),
        exp(log_estimates["P(relevant|position)"]))