code: def test_transform(self):
      passed = False
      # sometimes, LDA 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
          )
          model.update(self.corpus)
  
          # transform one document
          doc = list(corpus)[0]
          transformed = model[doc]
  
          vec = matutils.sparse2full(
              transformed, 2
          )  # convert to dense vector, for easier equality tests
          expected = [0.13, 0.87]
          # must contain the same values, up to re-ordering
          passed = np.allclose(sorted(vec), sorted(expected), atol=1e-1)
          if passed:
              break
          logging.warning(
              "LDA failed to converge on attempt %i (got %s, expected %s)",
              i,
              sorted(vec),
              sorted(expected),
          )
      self.assertTrue(passed)
  
imports: import logging
  import numbers
  import os
  import unittest
  import copy
  import numpy
  from numpy.testing import assert_allclose
  from gensim.corpora import mmcorpus, Dictionary
  from gensim.models import ldamodel, ldamulticore
  from gensim import matutils, utils
  from gensim.test import basetmtests
  from gensim.test.utils import datapath, get_tmpfile, common_texts
label_desc: Use image node_id 3 for calling the TestLdaModel obj's underlying member method code with example usage: obj.test_transform() without return types