code: def test_transform(self):
      passed = False
      # sometimes, training gets stuck at a local minimum
      # in that case try re-training the model from scratch, hoping for a
      # better random initialization
      for i in range(25):  # restart at most 5 times
          # create the transformation model
          model = self.class_(
              id2word=dictionary,
              num_topics=2,
              passes=100,
              random_state=0,
          )
          model.update(corpus, author2doc)
  
          jill_topics = model.get_author_topics("jill")
  
          # NOTE: this test may easily fail if the author-topic model is altered in any way. The model's
          # output is sensitive to a lot of things, like the scheduling of the updates, or like the
          # author2id (because the random initialization changes when author2id changes). If it does
          # fail, simply be aware of whether we broke something, or if it just naturally changed the
          # output of the model slightly.
          vec = matutils.sparse2full(
              jill_topics, 2
          )  # convert to dense vector, for easier equality tests
          expected = [0.91, 0.08]
          # must contain the same values, up to re-ordering
          passed = np.allclose(sorted(vec), sorted(expected), atol=1e-1)
          if passed:
              break
          logging.warning(
              "Author-topic model failed to converge on attempt %i (got %s, expected %s)",
              i,
              sorted(vec),
              sorted(expected),
          )
      self.assertTrue(passed)
  
imports: import logging
  import unittest
  import numbers
  from os import remove
  import numpy
  from gensim.corpora import mmcorpus, Dictionary
  from gensim.models import atmodel
  from gensim import matutils
  from gensim.test import basetmtests
  from gensim.test.utils import datapath, get_tmpfile, common_texts, common_dictionary, common_corpus
  from gensim.matutils import jensen_shannon
label_desc: Use image node_id 2 for calling the TestAuthorTopicModel obj's underlying member method code with example usage: obj.test_transform() without return types