class_docstr: Online Non-Negative Matrix Factorization.
  
  `Renbo Zhao et al :"Online Nonnegative Matrix Factorization with Outliers" <https://arxiv.org/abs/1604.02634>`_
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docstr: Get the topic distribution for the given document.
  
  Parameters
  ----------
  bow : list of (int, float)
      The document in BOW format.
  minimum_probability : float
      If `normalize` is True, topics with smaller probabilities are filtered out.
      If `normalize` is False, topics with smaller factors are filtered out.
      If set to None, a value of 1e-8 is used to prevent 0s.
  normalize: bool or None, optional
      Whether to normalize the result. Allows for estimation of perplexity, coherence, e.t.c.
  
  Returns
  -------
  list of (int, float)
      Topic distribution for the whole document. Each element in the list is a pair of a topic's id, and
      the probability that was assigned to it.
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code: def get_document_topics(
      self, bow, minimum_probability=None, normalize=None
  ):
      """Get the topic distribution for the given document.
  
      Parameters
      ----------
      bow : list of (int, float)
          The document in BOW format.
      minimum_probability : float
          If `normalize` is True, topics with smaller probabilities are filtered out.
          If `normalize` is False, topics with smaller factors are filtered out.
          If set to None, a value of 1e-8 is used to prevent 0s.
      normalize: bool or None, optional
          Whether to normalize the result. Allows for estimation of perplexity, coherence, e.t.c.
  
      Returns
      -------
      list of (int, float)
          Topic distribution for the whole document. Each element in the list is a pair of a topic's id, and
          the probability that was assigned to it.
  
      """
      if minimum_probability is None:
          minimum_probability = self.minimum_probability
      minimum_probability = max(minimum_probability, 1e-8)
  
      # if the input vector is a corpus, return a transformed corpus
      is_corpus, corpus = utils.is_corpus(bow)
  
      if is_corpus:
          kwargs = dict(minimum_probability=minimum_probability)
          return self._apply(corpus, **kwargs)
  
      v = matutils.corpus2csc([bow], self.num_tokens)
      h = self._solveproj(v, self._W, v_max=np.inf)
  
      if normalize is None:
          normalize = self.normalize
      if normalize:
          the_sum = h.sum()
          if the_sum:
              h /= the_sum
  
      return [
          (idx, proba)
          for idx, proba in enumerate(h[:, 0])
          if not minimum_probability or proba > minimum_probability
      ]
  
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 9 for calling the Nmf obj's underlying member method code with example usage: obj.get_document_topics(bow, minimum_probability, normalize) and returns: unknown, self