code: def test_topic_top_documents(lsi_model, svd_model, document_data):
      lsi_top_docs_df = lsi_model.top_documents_per_topic(
          text_docs=document_data, num_docs=2
      )
      assert isinstance(
          lsi_top_docs_df, pd.DataFrame
      ), "Output is not of the expected return type of Pandas data frame"
      assert lsi_top_docs_df.shape == (
          2,
          lsi_model.num_topics,
      ), "Top documents per topic function return value is the incorrect size"
  
      svd_top_docs_df = svd_model.top_documents_per_topic(
          text_docs=document_data, num_docs=2
      )
      svd_top_docs_df_summarized = svd_model.top_documents_per_topic(
          text_docs=document_data, summarize_docs=True, num_docs=2
      )
      svd_top_docs_df_summarized_words = (
          svd_model.top_documents_per_topic(
              text_docs=document_data,
              summarize_docs=True,
              summary_words=15,
              num_docs=2,
          )
      )
      assert not svd_top_docs_df.equals(
          svd_top_docs_df_summarized
      ), "Output of top documents per topic function for non-summarized documents is incorrectly equal to output of the function with summarized documents"
      assert not svd_top_docs_df_summarized.equals(
          svd_top_docs_df_summarized_words
      ), "Output of top documents per topic function for 10-word-summarized documents is incorrectly equal to output of the function with 15-word-summarized documents"
      assert svd_top_docs_df.shape == (
          2,
          4,
      ), "Top documents per topic function return value is the incorrect size"
  
imports: import pandas
  import numpy
  import matplotlib
  import sklearn
  import pytest
  from data_describe.text.topic_modeling import topic_model
label_desc: Use image node_id 17 for calling a global function with example usage: test_topic_top_documents(lsi_model, svd_model, document_data) without return types