code: def test_multiple_summary(nlp: Language):
      """
      Summarization produces consistent results when called across multiple
      docs.
      """
      texts = []
  
      with open("dat/lee.txt", "r") as f:
          text = f.read()
          texts.append(text)
  
      with open("dat/mih.txt", "r") as f:
          text = f.read()
          texts.append(text)
  
      docs = [nlp(text) for text in texts]
  
      trace1 = [
          [sent_dist.sent_id, list(sent_dist.phrases)]
          for sent_dist in docs[0]._.textrank.calc_sent_dist(
              limit_phrases=10
          )
          if not sent_dist.empty()
      ]
  
      trace2 = [
          [sent_dist.sent_id, list(sent_dist.phrases)]
          for sent_dist in docs[1]._.textrank.calc_sent_dist(
              limit_phrases=10
          )
          if not sent_dist.empty()
      ]
  
      assert trace1 != trace2
  
file_path: pytextrank/tests/test_topicrank.py
imports: from spacy.language import Language
  from spacy.tokens import Span, Doc
  import spacy
  import sys
  from pytextrank.topicrank import TopicRankFactory
label_desc: Use image node_id 4 for calling a global function with example usage: test_multiple_summary(nlp) without return types