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: Fit the sequential model topic-wise.
  
  Parameters
  ----------
  topic_suffstats : numpy.ndarray
      Sufficient statistics of the current model, expected shape (`self.vocab_len`, `num_topics`).
  
  Returns
  -------
  float
      The sum of the optimized lower bounds for all topics.
docstr_tok: ["Fit","the","sequential","model","topic-wise",".","Parameters","--","--","--","--","--","topic_suffstats",":","numpy.ndarray","Sufficient","statistics","of","the","current","model",",","expected","shape","(","`","self.vocab_len","`",",","`","num_topics","`",")",".","Returns","--","--","--","-","float","The","sum","of","the","optimized","lower","bounds","for","all","topics","."]
code: def fit_lda_seq_topics(self, topic_suffstats):
      """Fit the sequential model topic-wise.
  
      Parameters
      ----------
      topic_suffstats : numpy.ndarray
          Sufficient statistics of the current model, expected shape (`self.vocab_len`, `num_topics`).
  
      Returns
      -------
      float
          The sum of the optimized lower bounds for all topics.
  
      """
      lhood = 0
  
      for k, chain in enumerate(self.topic_chains):
          logger.info("Fitting topic number %i", k)
          lhood_term = sslm.fit_sslm(chain, topic_suffstats[k])
          lhood += lhood_term
  
      return lhood
  
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 7 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.fit_lda_seq_topics(topic_suffstats) and returns: lhood