I have built a pipelined lda model using pyspark's ml.  The code is below:tokenizer = Tokenizer(inputCol='formatted_transcript',  outputCol='words')stopWordsRemover = StopWordsRemover(inputCol=words,  outputCol=filtered)vectorizer = CountVectorizer(inputCol=filtered,  outputCol=features, vocabSize=40000, minDF=5.0) lda = LDA(k=10, maxIter=10)pipeline = Pipeline(stages=[tokenizer, stopWordsRemover, vectorizer,  lda])pipelineModel = pipeline.fit(DF_with_formatted)When I print the results, i.e.print(pipelineModel.stages[3].describeTopics(5).show(truncate=False))I see the termIndices and termWeights, but not the actual words.  How do I obtain the words corresponding to the termIndices?
Words from LDA output pyspark ml
nlp;lda;pyspark