code: def update_timestep(self, modelparams, expparams):
          r
          return np.tile(modelparams, (expparams.shape[0],1,1)).transpose((1,2,0))
docs: r"""
          Returns a set of model parameter vectors that is the update of an
          input set of model parameter vectors, such that the new models are
          conditioned on a particular experiment having been performed.
          By default, this is the trivial function
          :math:`\vec{x}(t_{k+1}) = \vec{x}(t_k)`.
          
          :param np.ndarray modelparams: Set of model parameter vectors to be
              updated.
          :param np.ndarray expparams: An experiment parameter array describing
              the experiment that was just performed.
          
          :return np.ndarray: Array of shape
              ``(n_models, n_modelparams, n_experiments)`` describing the update
              of each model according to each experiment.