class_docstr: Model for `Latent Semantic Indexing
  <https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing>`_.
  
  The decomposition algorithm is described in `"Fast and Faster: A Comparison of Two Streamed
  Matrix Decomposition Algorithms" <https://arxiv.org/pdf/1102.5597.pdf>`_.
  
  Notes
  -----
  * :attr:`gensim.models.lsimodel.LsiModel.projection.u` - left singular vectors,
  * :attr:`gensim.models.lsimodel.LsiModel.projection.s` - singular values,
  * ``model[training_corpus]`` - right singular vectors (can be reconstructed if needed).
  
  See Also
  --------
  `FAQ about LSI matrices
  <https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ#q4-how-do-you-output-the-u-s-vt-matrices-of-lsi>`_.
  
  Examples
  --------
  .. sourcecode:: pycon
  
      >>> from gensim.test.utils import common_corpus, common_dictionary, get_tmpfile
      >>> from gensim.models import LsiModel
      >>>
      >>> model = LsiModel(common_corpus[:3], id2word=common_dictionary)  # train model
      >>> vector = model[common_corpus[4]]  # apply model to BoW document
      >>> model.add_documents(common_corpus[4:])  # update model with new documents
      >>> tmp_fname = get_tmpfile("lsi.model")
      >>> model.save(tmp_fname)  # save model
      >>> loaded_model = LsiModel.load(tmp_fname)  # load model
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docstr: Print (to log) the most salient words of the first `num_topics` topics.
  
  Unlike :meth:`~gensim.models.lsimodel.LsiModel.print_topics`, this looks for words that are significant for
  a particular topic *and* not for others. This *should* result in a
  more human-interpretable description of topics.
  
  Alias for :func:`~gensim.models.lsimodel.print_debug`.
  
  Parameters
  ----------
  num_topics : int, optional
      The number of topics to be selected (ordered by significance).
  num_words : int, optional
      The number of words to be included per topics (ordered by significance).
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code: def print_debug(self, num_topics=5, num_words=10):
      """Print (to log) the most salient words of the first `num_topics` topics.
  
      Unlike :meth:`~gensim.models.lsimodel.LsiModel.print_topics`, this looks for words that are significant for
      a particular topic *and* not for others. This *should* result in a
      more human-interpretable description of topics.
  
      Alias for :func:`~gensim.models.lsimodel.print_debug`.
  
      Parameters
      ----------
      num_topics : int, optional
          The number of topics to be selected (ordered by significance).
      num_words : int, optional
          The number of words to be included per topics (ordered by significance).
  
      """
      # only wrap the module-level fnc
      print_debug(
          self.id2word,
          self.projection.u,
          self.projection.s,
          range(min(num_topics, len(self.projection.u.T))),
          num_words=num_words,
      )
  
imports: import logging
  import sys
  import time
  import numpy
  import scipy.linalg
  import scipy.sparse
  from scipy.sparse import sparsetools
  from gensim import interfaces, matutils, utils
  from gensim.models import basemodel
  from gensim.utils import is_empty
label_desc: Use image node_id 8 for calling the LsiModel obj's underlying member method code with example usage: obj.print_debug(num_topics, num_words) without return types