code: def f_obs(x, *args):
      """Function which we are optimising for minimizing obs.
  
      Parameters
      ----------
      x : list of float
          The obs values for this word.
      sslm : :class:`~gensim.models.ldaseqmodel.sslm`
          The State Space Language Model for DTM.
      word_counts : list of int
          Total word counts for each time slice.
      totals : list of int of length `len(self.time_slice)`
          The totals for each time slice.
      mean_deriv_mtx : list of float
          Mean derivative for each time slice.
      word : int
          The word's ID.
      deriv : list of float
          Mean derivative for each time slice.
  
      Returns
      -------
      list of float
          The value of the objective function evaluated at point `x`.
  
      """
      sslm, word_counts, totals, mean_deriv_mtx, word, deriv = args
      # flag
      init_mult = 1000
  
      T = len(x)
      val = 0
      term1 = 0
      term2 = 0
  
      # term 3 and 4 for DIM
      term3 = 0
      term4 = 0
  
      sslm.obs[word] = x
      sslm.mean[word], sslm.fwd_mean[word] = sslm.compute_post_mean(
          word, sslm.chain_variance
      )
  
      mean = sslm.mean[word]
      variance = sslm.variance[word]
  
      # only used for DIM mode
      # w_phi_l = sslm.w_phi_l[word]
      # m_update_coeff = sslm.m_update_coeff[word]
  
      for t in range(1, T + 1):
          mean_t = mean[t]
          mean_t_prev = mean[t - 1]
  
          val = mean_t - mean_t_prev
          term1 += val * val
          term2 += (
              word_counts[t - 1] * mean_t
              - totals[t - 1]
              * np.exp(mean_t + variance[t] / 2)
              / sslm.zeta[t - 1]
          )
  
          model = "DTM"
          if model == "DIM":
              # stuff happens
              pass
  
      if sslm.chain_variance > 0.0:
          term1 = -(term1 / (2 * sslm.chain_variance))
          term1 = term1 - mean[0] * mean[0] / (
              2 * init_mult * sslm.chain_variance
          )
      else:
          term1 = 0.0
  
      final = -(term1 + term2 + term3 + term4)
  
      return final
  
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 1 for calling a global function with example usage: f_obs(x) and returns: final