code: def generate_observation_trajectory(self, s_t):
          
  
          # Determine number of samples to generate.
          T = s_t.shape[0]
  
          o_t = np.zeros([T], dtype=config.dtype)
          for t in range(T):
              s = s_t[t]
              o_t[t] = self.sigmas[s] * np.random.randn() + self.means[s]
          return o_t
docs: Generate synthetic observation data from a given state sequence.
  
          Parameters
          ----------
          s_t : numpy.array with shape (T,) of int type
              s_t[t] is the hidden state sampled at time t
  
          Returns
          -------
          o_t : numpy.array with shape (T,) of type dtype
              o_t[t] is the observation associated with state s_t[t]
  
          Examples
          --------
  
          Generate an observation model and synthetic state trajectory.
  
          >>> nobs = 1000
          >>> output_model = GaussianOutputModel(nstates=3, means=[-1, 0, +1], sigmas=[0.5, 1, 2])
          >>> s_t = np.random.randint(0, output_model.nstates, size=[nobs])
  
          Generate a synthetic trajectory
  
          >>> o_t = output_model.generate_observation_trajectory(s_t)