Problem with model.transform() after Outlier reduction procedure

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Hello,
I am trying to extract topics from a list of texts. Since my data probably lacks a bit of quality and half of the texts were classified as outliers, I proceeded with the outlier reduction stage after the topic extraction one.
This is the code:

```
self.model = BERTopic(
language="italian",
top_n_words=10,
n_gram_range=(1, 1),
min_topic_size=40,
nr_topics="auto",
embedding_model=embedding_model,
seed_topic_list=seed_topic_list,
calculate_probabilities=False,
# verbose=self.verbose
)
# Fit the model to the documents
topics, _ = self.model.fit_transform(self.history_tickets)

# Use this method to reduce the number of outliers taken, and get the new topics
new_topics = self.model.reduce_outliers(self.history_tickets, topics, strategy="c-tf-idf")
# Then, update the topics to the ones that considered the new data
self.model.update_topics(self.history_tickets, topics=new_topics)
```

And it works fine. However, when I proceed with the extraction of the topic of a single text (that was already part of self.history_tickets):

`prediction = self.model.transform([text])[0][0]`

the prediction is in most cases -1.
What is the problem? Should I proceed with reduce_outliers also after the single prediction?

Thanks in advance,

Start by reproducing the issue with the shown BERTopic fit_transform, reduce_outliers, update_topics, and transform sequence. Read the behavior of reduce_outliers and update_topics alongside transform, then verify whether transforming an already-seen text should return its updated topic rather than -1. Done means the expected behavior is confirmed and covered by a regression test or clearly documented.

Written by the indexing model from the issue text.