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: Compute the expected log probability given values of m.
  
  The appendix describes the Expectation of log-probabilities in equation 5 of the DTM paper;
  The below implementation is the result of solving the equation and is implemented as in the original
  Blei DTM code.
  
  Returns
  -------
  numpy.ndarray of float
      The expected value for the log probabilities for each word and time slice.
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code: def compute_expected_log_prob(self):
      """Compute the expected log probability given values of m.
  
      The appendix describes the Expectation of log-probabilities in equation 5 of the DTM paper;
      The below implementation is the result of solving the equation and is implemented as in the original
      Blei DTM code.
  
      Returns
      -------
      numpy.ndarray of float
          The expected value for the log probabilities for each word and time slice.
  
      """
      for (w, t), val in np.ndenumerate(self.e_log_prob):
          self.e_log_prob[w][t] = self.mean[w][t + 1] - np.log(
              self.zeta[t]
          )
      return self.e_log_prob
  
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 5 for calling the sslm obj's underlying member method code with example usage: obj.compute_expected_log_prob() and returns: self