class_docstr: Train and use Online Latent Dirichlet Allocation model as presented in
  `'Online Learning for LDA' by Hoffman et al.`_
  
  Examples
  -------
  Initialize a model using a Gensim corpus
  
  .. sourcecode:: pycon
  
      >>> from gensim.test.utils import common_corpus
      >>>
      >>> lda = LdaModel(common_corpus, num_topics=10)
  
  You can then infer topic distributions on new, unseen documents.
  
  .. sourcecode:: pycon
  
      >>> doc_bow = [(1, 0.3), (2, 0.1), (0, 0.09)]
      >>> doc_lda = lda[doc_bow]
  
  The model can be updated (trained) with new documents.
  
  .. sourcecode:: pycon
  
      >>> # In practice (corpus =/= initial training corpus), but we use the same here for simplicity.
      >>> other_corpus = common_corpus
      >>>
      >>> lda.update(other_corpus)
  
  Model persistency is achieved through :meth:`~gensim.models.ldamodel.LdaModel.load` and
  :meth:`~gensim.models.ldamodel.LdaModel.save` methods.
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docstr: Get a string representation of the current object.
  
  Returns
  -------
  str
      Human readable representation of the most important model parameters.
docstr_tok: ["Get","a","string","representation","of","the","current","object",".","Returns","--","--","--","-","str","Human","readable","representation","of","the","most","important","model","parameters","."]
code: def __str__(self):
      """Get a string representation of the current object.
  
      Returns
      -------
      str
          Human readable representation of the most important model parameters.
  
      """
      return (
          "%s<num_terms=%s, num_topics=%s, decay=%s, chunksize=%s>"
          % (
              self.__class__.__name__,
              self.num_terms,
              self.num_topics,
              self.decay,
              self.chunksize,
          )
      )
  
imports: import logging
  import numbers
  import os
  import time
  from collections import defaultdict
  import numpy
  from scipy.special import gammaln, psi
  from scipy.special import polygamma
  from gensim import interfaces, utils, matutils
  from gensim.matutils import kullback_leibler, hellinger, jaccard_distance, jensen_shannon, dirichlet_expectation, logsumexp, mean_absolute_difference
  from gensim.models import basemodel, CoherenceModel
  from gensim.models.callbacks import Callback
label_desc: Use image node_id 3 for calling the LdaModel obj's underlying member method code with example usage: obj.__str__() and returns: unknown