code: def test_top_topic_terms_weights(vectorizer, model):
      observed = list(
          model.top_topic_terms(
              vectorizer.id_to_term, topics=-1, top_n=10, weights=True
          )
      )
      assert isinstance(observed[0][1][0], tuple)
      for topic_idx, term_weights in observed:
          for i in range(len(term_weights) - 1):
              assert term_weights[i][1] >= term_weights[i + 1][1]
  
imports: import numpy
  import pytest
  from sklearn.decomposition import NMF, LatentDirichletAllocation, TruncatedSVD
  from textacy import Corpus
  from textacy import extract
  from textacy.tm import TopicModel
  from textacy.representations import Vectorizer
label_desc: Use image node_id 14 for calling a global function with example usage: test_top_topic_terms_weights(vectorizer, model) without return types