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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 calculate_length_statistics(source_iterables: Sequence[Iterable[Any]], target_iterable: Iterable[Any], max_seq_len_source: int, max_seq_len_target: int) -> 'LengthStatistics': """ Returns mean and standard deviation of target-to-source length ratios of parallel corpus. :param source_iterables: Source sequence readers. :param target_iterable: Target sequence reader. :param max_seq_len_source: Maximum source sequence length. :param max_seq_len_target: Maximum target sequence length. :return: The number of sentences as well as the mean and standard deviation of target to source length ratios. """

    mean_and_variance = OnlineMeanAndVariance()

    for sources, target in parallel_iter(source_iterables, target_iterable):
        source_len = len(sources[0])
        target_len = len(target)
        if source_len > max_seq_len_source or target_len > max_seq_len_target:
            continue

        length_ratio = target_len / source_len
        mean_and_variance.update(length_ratio)

    return LengthStatistics(mean_and_variance.count, mean_and_variance.mean, mean_and_variance.std)