docstr: Trains a scorer that can score documents based on similarity to a
  seed set of documents represented by topic_ids and doc_ids.
  
  NOTE: The score method currently employs the use of LocalOutLierFactor, which
  means you should not try to score documents that were used in training. Only
  new, unseen documents should be scored for similarity.
  REFERENCE:
  https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor
  
  Args:
      topic_ids(list of ints): list of topid IDs where each id is in the range
                               of range(self.n_topics).  Documents associated
                               with these topic_ids will be used as seed set.
      doc_ids (list of ints): list of document IDs where each id is an index
                              into self.doctopics.  Documents associated
                              with these doc_ids will be used as seed set.
  Returns:
      None
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code: def train_scorer(self, topic_ids=[], doc_ids=[], n_neighbors=20):
      """
      Trains a scorer that can score documents based on similarity to a
      seed set of documents represented by topic_ids and doc_ids.
  
      NOTE: The score method currently employs the use of LocalOutLierFactor, which
      means you should not try to score documents that were used in training. Only
      new, unseen documents should be scored for similarity.
      REFERENCE:
      https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor
  
      Args:
          topic_ids(list of ints): list of topid IDs where each id is in the range
                                   of range(self.n_topics).  Documents associated
                                   with these topic_ids will be used as seed set.
          doc_ids (list of ints): list of document IDs where each id is an index
                                  into self.doctopics.  Documents associated
                                  with these doc_ids will be used as seed set.
      Returns:
          None
      """
      from sklearn.neighbors import LocalOutlierFactor
  
      clf = LocalOutlierFactor(
          n_neighbors=n_neighbors, novelty=True, contamination=0.1
      )
      probs = self.get_doctopics(topic_ids=topic_ids, doc_ids=doc_ids)
      clf.fit(probs)
      self.scorer = clf
      return
  
imports: import time
  from ..None import utils
  from ..imports import *
  from .None import preprocessor
  from .None import textutils
label_desc: Use image node_id 18 for calling the TopicModel obj's underlying member method code with example usage: obj.train_scorer(topic_ids, doc_ids, n_neighbors) without return types