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 the topic distribution for the given document.
  
  Wraps :meth:`~gensim.models.ldamodel.LdaModel.get_document_topics` to support an operator style call.
  Uses the model's current state (set using constructor arguments) to fill in the additional arguments of the
  wrapper method.
  
  Parameters
  ---------
  bow : list of (int, float)
      The document in BOW format.
  eps : float, optional
      Topics with an assigned probability lower than this threshold will be discarded.
  
  Returns
  -------
  list of (int, float)
      Topic distribution for the given document. Each topic is represented as a pair of its ID and the probability
      assigned to it.
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code: def __getitem__(self, bow, eps=None):
      """Get the topic distribution for the given document.
  
      Wraps :meth:`~gensim.models.ldamodel.LdaModel.get_document_topics` to support an operator style call.
      Uses the model's current state (set using constructor arguments) to fill in the additional arguments of the
      wrapper method.
  
      Parameters
      ---------
      bow : list of (int, float)
          The document in BOW format.
      eps : float, optional
          Topics with an assigned probability lower than this threshold will be discarded.
  
      Returns
      -------
      list of (int, float)
          Topic distribution for the given document. Each topic is represented as a pair of its ID and the probability
          assigned to it.
  
      """
      return self.get_document_topics(
          bow, eps, self.minimum_phi_value, self.per_word_topics
      )
  
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 22 for calling the LdaModel obj's underlying member method code with example usage: obj.__getitem__(bow, eps) and returns: self