Merge Multiple Fitted Models - Error on Viewing Hierarchical Topics

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**Problem Overview**
When using the ```merged_model = BERTopic.merge_models([topic_model_1, topic_model_2])``` command the produced merged topic model cannot be visualised as a hierarchical topic model anymore, even if the constituent models can be.

**Error Code**
```
hierarchical_topics = merged_model.hierarchical_topics(docs, linkage_function = linkage_function)
Traceback (most recent call last):

Cell In[14], line 1
hierarchical_topics = merged_model.hierarchical_topics(docs, linkage_function = linkage_function)

File ~/anaconda3/envs/tf/lib/python3.9/site-packages/bertopic/_bertopic.py:975 in hierarchical_topics
embeddings = self.c_tf_idf_[self._outliers:]

TypeError: 'NoneType' object is not subscriptable
```

**Minimum Working Example**

```python
from umap import UMAP
from bertopic import BERTopic
from datasets import load_dataset
from sklearn.datasets import fetch_20newsgroups

docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))["data"]

# Create topic models
umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42)
topic_model_1 = BERTopic(umap_model=umap_model, min_topic_size=20).fit(docs[0:1000])
topic_model_2 = BERTopic(umap_model=umap_model, min_topic_size=20).fit(docs[1000:2000])

# Combine all models into one
merged_model = BERTopic.merge_models([topic_model_1, topic_model_2])

# #Visualise Hierarchical Topic Model
linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)

#Use fitted model to extract hierarchies
hierarchical_topics = merged_model.hierarchical_topics(docs, linkage_function = linkage_function)

#Visualise Hierarchies
fig = merged_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)
fig.write_html("merged_model.html")
```

Start with the provided minimum working example and inspect _bertopic.py at hierarchical_topics, especially the c-TF-IDF access shown in the traceback, alongside merge_models. Done means a model produced by merge_models can run hierarchical_topics and then visualize_hierarchy without the reported NoneType error.

Written by the indexing model from the issue text.