class_docstr: Encapsulate the inner State Space Language Model for DTM.
  
  Some important attributes of this class:
  
      * `obs` is a matrix containing the document to topic ratios.
      * `e_log_prob` is a matrix containing the topic to word ratios.
      * `mean` contains the mean values to be used for inference for each word for a time slice.
      * `variance` contains the variance values to be used for inference of word in a time slice.
      * `fwd_mean` and`fwd_variance` are the forward posterior values for the mean and the variance.
      * `zeta` is an extra variational parameter with a value for each time slice.
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docstr: Update the Zeta variational parameter.
  
  Zeta is described in the appendix and is equal to sum (exp(mean[word] + Variance[word] / 2)),
  over every time-slice. It is the value of variational parameter zeta which maximizes the lower bound.
  
  Returns
  -------
  list of float
      The updated zeta values for each time slice.
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code: def update_zeta(self):
      """Update the Zeta variational parameter.
  
      Zeta is described in the appendix and is equal to sum (exp(mean[word] + Variance[word] / 2)),
      over every time-slice. It is the value of variational parameter zeta which maximizes the lower bound.
  
      Returns
      -------
      list of float
          The updated zeta values for each time slice.
  
      """
      for j, val in enumerate(self.zeta):
          self.zeta[j] = np.sum(
              np.exp(self.mean[:, j + 1] + self.variance[:, j + 1] / 2)
          )
      return self.zeta
  
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 2 for calling the sslm obj's underlying member method code with example usage: obj.update_zeta() and returns: self