code: def propose(self):
          
  
          # Locally store size of matrix
          dims = self.stochastic.value.shape
  
          # Add normal deviate to value and symmetrize
          dev = rnormal(
              0,
              self.adaptive_scale_factor *
              self.proposal_sd,
              size=dims)
          symmetrize(dev)
  
          # Replace
          self.stochastic.value = dev + self.stochastic.value
docs: Proposals for positive definite matrix using random walk deviations on the Cholesky
          factor of the current value.