option to recalculate c_tf_idf_, topic_representations_ and representative_docs_ after merging after merging models

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Hello!
I have been using the merged models to avoid RAM limitations.
After merging my models into a new model, I found that there are no representative documents in model.get_topic_info() and also model.hierarchical_topics(docs) is not working. I solved the problem with the help of:

``` python
# Create a df with the following columns ["Document", "Topic", "ID"]
docs = data["text_caption"].values
topics = data["topic"].values
ids = range(len(docs))
images = None

documents = pd.DataFrame({"Document": docs, "Topic": topics, "ID": ids, "Image": images})
documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})

merged_model_8.c_tf_idf_, words = merged_model_8._c_tf_idf(documents_per_topic)

merged_model_8.topic_representations_ = merged_model_8._extract_words_per_topic(words, documents, merged_model_8.c_tf_idf_, calculate_aspects=False)
merged_model_8._save_representative_docs(documents)
```

It would be good to have a function/option that does this automatically.

Start with the model merge flow and the mentioned entry points: get_topic_info(), hierarchical_topics(docs), _c_tf_idf, _extract_words_per_topic, and _save_representative_docs. Trace how merged models retain or rebuild these representations, then verify that the resulting model has representative documents and that hierarchical_topics(docs) works without manual recalculation.

Written by the indexing model from the issue text.