class_docstr: Estimate Dynamic Topic Model parameters based on a training corpus.
class_docstr_tok: ["Estimate","Dynamic","Topic","Model","parameters","based","on","a","training","corpus","."]
docstr: Get the list of words most relevant to the given topic.
  
  Parameters
  ----------
  topic : int
      The index of the topic to be inspected.
  time : int, optional
      The time slice in which we are interested in (since topics evolve over time, it is expected that the most
      relevant words will also gradually change).
  top_terms : int, optional
      Number of words associated with the topic to be returned.
  
  Returns
  -------
  list of (str, float)
      The representation of this topic. Each element in the list includes the word itself, along with the
      probability assigned to it by the topic.
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code: def print_topic(self, topic, time=0, top_terms=20):
      """Get the list of words most relevant to the given topic.
  
      Parameters
      ----------
      topic : int
          The index of the topic to be inspected.
      time : int, optional
          The time slice in which we are interested in (since topics evolve over time, it is expected that the most
          relevant words will also gradually change).
      top_terms : int, optional
          Number of words associated with the topic to be returned.
  
      Returns
      -------
      list of (str, float)
          The representation of this topic. Each element in the list includes the word itself, along with the
          probability assigned to it by the topic.
  
      """
      topic = self.topic_chains[topic].e_log_prob
      topic = np.transpose(topic)
      topic = np.exp(topic[time])
      topic = topic / topic.sum()
      bestn = matutils.argsort(topic, top_terms, reverse=True)
      beststr = [(self.id2word[id_], topic[id_]) for id_ in bestn]
      return beststr
  
imports: import logging
  import numpy
  from scipy.special import digamma, gammaln
  from scipy import optimize
  from gensim import utils, matutils
  from gensim.models import ldamodel
label_desc: Use image node_id 10 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.print_topic(topic, time, top_terms) and returns: beststr