text: Title: Merge Multiple Fitted Models - Error on Viewing Hierarchical Topics
  Body: **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")
  ``` 