code: def update(self, other, soft=False, tau=0.1):
      params_self, params_other = (
          self.pc.parameters_list(),
          other.pc.parameters_list(),
      )
      for x, y in zip(params_self, params_other):
          target_values = (
              ((1 - tau) * x.as_array() + tau * y.as_array())
              if soft
              else y.as_array()
          )
          x.set_value(target_values)
  
file_path: dynet/examples/reinforcement-learning/network.py
imports: import dynet
label_desc: Use image node_id 2 for calling the Network obj's underlying member method code with example usage: obj.update(other, soft, tau) without return types