How to do Hierarchical Topic Modeling on Merged Model?

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### Desribe the bug

Hi @MaartenGr
I am trying to create visualization of hierarchical topic modeling on two topic models merged using .merge_models.

```
hierarchical_topics_merged = merged_model.hierarchical_topics(docs_1+docs_2)
```

It produces the following error:

```
2024-09-16 09:47:47,878 - BERTopic - WARNING: No c-TF-IDF matrix was found despite it is supposed to be used (`use_ctfidf` is True). Defaulting to semantic embeddings.
---------------------------------------------------------------------------
NotFittedError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
----> 1 hierarchical_topics_merged = merged_model.hierarchical_topics(docs_3)

2 frames
[/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py](https://localhost:8080/#) in _check_vocabulary(self)
506 self._validate_vocabulary()
507 if not self.fixed_vocabulary_:
--> 508 raise NotFittedError("Vocabulary not fitted or provided")
509
510 if len(self.vocabulary_) == 0:

NotFittedError: Vocabulary not fitted or provided
```

How do I visualize merged models?

Thanks!

### BERTopic Version

v0.16.3

Start by reproducing the call to merged_model.hierarchical_topics(docs_1+docs_2) after merge_models, then inspect the c-TF-IDF handling and the NotFittedError from sklearn's text feature extraction. Check the hierarchical_topics and merge_models entry points. Done means either merged models can be visualized successfully or the supported workflow and limitation are documented.

Written by the indexing model from the issue text.