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 most relevant words for a topic, for each timeslice. This can be used to inspect the evolution of a
  topic through time.
  
  Parameters
  ----------
  topic : int
      The index of the topic.
  top_terms : int, optional
      Number of most relevant words associated with the topic to be returned.
  
  Returns
  -------
  list of list of str
      Top `top_terms` relevant terms for the topic for each time slice.
docstr_tok: ["Get","the","most","relevant","words","for","a","topic",",","for","each","timeslice",".","This","can","be","used","to","inspect","the","evolution","of","a","topic","through","time",".","Parameters","--","--","--","--","--","topic",":","int","The","index","of","the","topic",".","top_terms",":","int",",","optional","Number","of","most","relevant","words","associated","with","the","topic","to","be","returned",".","Returns","--","--","--","-","list","of","list","of","str","Top","`","top_terms","`","relevant","terms","for","the","topic","for","each","time","slice","."]
code: def print_topic_times(self, topic, top_terms=20):
      """Get the most relevant words for a topic, for each timeslice. This can be used to inspect the evolution of a
      topic through time.
  
      Parameters
      ----------
      topic : int
          The index of the topic.
      top_terms : int, optional
          Number of most relevant words associated with the topic to be returned.
  
      Returns
      -------
      list of list of str
          Top `top_terms` relevant terms for the topic for each time slice.
  
      """
      topics = []
      for time in range(self.num_time_slices):
          topics.append(self.print_topic(topic, time, top_terms))
  
      return topics
  
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 8 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.print_topic_times(topic, top_terms) and returns: topics