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 every topic.
  
  Parameters
  ----------
  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 most relevant words to be returned for each topic.
  
  Returns
  -------
  list of list of (str, float)
      Representation of all topics. Each of them is represented by a list of pairs of words and their assigned
      probability.
docstr_tok: ["Get","the","most","relevant","words","for","every","topic",".","Parameters","--","--","--","--","--","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","most","relevant","words","to","be","returned","for","each","topic",".","Returns","--","--","--","-","list","of","list","of","(","str",",","float",")","Representation","of","all","topics",".","Each","of","them","is","represented","by","a","list","of","pairs","of","words","and","their","assigned","probability","."]
code: def print_topics(self, time=0, top_terms=20):
      """Get the most relevant words for every topic.
  
      Parameters
      ----------
      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 most relevant words to be returned for each topic.
  
      Returns
      -------
      list of list of (str, float)
          Representation of all topics. Each of them is represented by a list of pairs of words and their assigned
          probability.
  
      """
      return [
          self.print_topic(topic, time, top_terms)
          for topic in range(self.num_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 9 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.print_topics(time, top_terms) and returns: unknown