code: def update(self, τ: float = 1.0, update_indicators=True, dampen=False):
          
  
          for n in self.nodes(data=True):
              n[1]["next_state"] = n[1]["update_function"](n)
  
          for n in self.nodes(data=True):
              n[1]["rv"].dataset = n[1]["next_state"]
  
          for n in self.nodes(data=True):
              for i in range(self.res):
                  self.s0[i][n[0]] = n[1]["rv"].dataset[i]
                  if dampen:
                      self.s0[i][f"∂({n[0]})/∂t"] = self.s0_original[
                          f"∂({n[0]})/∂t"
                      ] * exp(-τ * self.t)
              if update_indicators:
                  for indicator in n[1]["indicators"].values():
                      indicator.samples = np.random.normal(
                          indicator.mean * np.array(n[1]["rv"].dataset),
                          scale=0.01,
                      )
  
          self.t += self.Δt
docs: Advance the model by one time step.