How to get the respective topics, the name of each topic, the top n words of each topic and other data for news docs on which `transform()` is used?

Suppose I trained the model first and got the topics, representative docs etc of the training docs using .get_document_info():

topic_model = BERTopic(vectorizer_model=vectorizer_model, hdbscan_model=hdbscan_model, embedding_model=embedding_model)
topics, probs = topic_model.fit_transform(docs)
print(topic_model.get_document_info())

and now I am predicting topics over new_docs:

new_topics, new_probs = topic_model.transform(new_docs)

now how do I get the information which new_doc falls in which new_topic? Like how can I generate a list/df just same as .get_document_info() for the newer docs and its new topics?

Start with the documented get_document_info(), fit_transform(), and transform() entry points, comparing the information returned for training documents with the topic assignments returned for new_docs. Check the API or usage documentation for a supported way to present transformed documents, and consider the work done when the behavior or limitation is clearly documented with an equivalent result for new_docs.