class_docstr: Online Non-Negative Matrix Factorization.
  
  `Renbo Zhao et al :"Online Nonnegative Matrix Factorization with Outliers" <https://arxiv.org/abs/1604.02634>`_
class_docstr_tok: ["Online","Non-Negative","Matrix","Factorization",".","`","Renbo","Zhao","et","al",":","''","Online","Nonnegative","Matrix","Factorization","with","Outliers","''","<","https",":","\/\/arxiv.org\/abs\/1604.02634",">","`","_"]
docstr: Get the representation for a single topic. Words the integer IDs, in constrast to
  :meth:`~gensim.models.nmf.Nmf.show_topic` that represents words by the actual strings.
  
  Parameters
  ----------
  topicid : int
      The ID of the topic to be returned
  topn : int, optional
      Number of the most significant words that are associated with the topic.
  normalize: bool or None, optional
      Whether to normalize the result. Allows for estimation of perplexity, coherence, e.t.c.
  
  Returns
  -------
  list of (int, float)
      Word ID - probability pairs for the most relevant words generated by the topic.
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code: def get_topic_terms(self, topicid, topn=10, normalize=None):
      """Get the representation for a single topic. Words the integer IDs, in constrast to
      :meth:`~gensim.models.nmf.Nmf.show_topic` that represents words by the actual strings.
  
      Parameters
      ----------
      topicid : int
          The ID of the topic to be returned
      topn : int, optional
          Number of the most significant words that are associated with the topic.
      normalize: bool or None, optional
          Whether to normalize the result. Allows for estimation of perplexity, coherence, e.t.c.
  
      Returns
      -------
      list of (int, float)
          Word ID - probability pairs for the most relevant words generated by the topic.
  
      """
      topic = self._W[:, topicid]
  
      if normalize is None:
          normalize = self.normalize
      if normalize:
          topic /= topic.sum()
  
      bestn = matutils.argsort(topic, topn, reverse=True)
      return [(idx, topic[idx]) for idx in bestn]
  
imports: import collections.abc
  import logging
  import numpy
  import scipy.sparse
  from scipy.stats import halfnorm
  from gensim import interfaces
  from gensim import matutils
  from gensim import utils
  from gensim.interfaces import TransformedCorpus
  from gensim.models import basemodel, CoherenceModel
  from gensim.models.nmf_pgd import solve_h
label_desc: Use image node_id 6 for calling the Nmf obj's underlying member method code with example usage: obj.get_topic_terms(topicid, topn, normalize) and returns: unknown