**Use case**

Generate models once and reuse them.

**Describe the solution you'd like**

Add command line options:
```
--save file - write serialized models on disk after training and before generating the data; the user can set --limit to 0 to only train the models and save them without proceeding to data generation;
--load file - instead of training the models, load the prepared models from the file and start generating the data.
```

Serialization format: one byte for version (equals to 0), then simple binary serialization of all the models; the whole file is compressed with ZSTD level 1 using CompressedWriteBuffer (no need to allow any compression options to choose).
if (params.frequency_cutoff)
        {
            for (auto & elem : table)
            {
                Histogram & histogram = elem.getMapped();
                if (!histogram.total)
                    continue;
                if (histogram.total + histogram.count_end < params.frequency_cutoff)
                {
                    histogram.buckets.clear();
                    histogram.total = 0;
                }
                else
                {
                    Histogram::Buckets new_buckets;
                    UInt64 erased_count = 0;
                    for (const auto & bucket : histogram.buckets)
                    {
                        if (bucket.second >= params.frequency_cutoff)
                            new_buckets.emplace(bucket);
                        else
                            erased_count += bucket.second;
                    }
                    histogram.buckets.swap(new_buckets);
                    histogram.total -= erased_count;
                }
            }
        }
        if (params.frequency_add)
        {