docstr: Calculate squared Bellman residual with test dataset. The operations are the same as learn method above,
  except backpropagation.
docstr_tok: ["Calculate","squared","Bellman","residual","with","test","dataset",".","The","operations","are","the","same","as","learn","method","above",",","except","backpropagation","."]
code: def supervised_eval(self, obs, action, reward, next_obs, terminal):
      """Calculate squared Bellman residual with test dataset. The operations are the same as learn method above,
      except backpropagation.
      """
      cost = self.cal_bellman_residual(
          obs, action, reward, next_obs, terminal
      )
      cost.stop_gradient = True
      return cost
  
file_path: PARL/benchmark/fluid/offline-Q-learning/dqn.py
imports: import warnings
  import copy
  import paddle.fluid
  from parl.core.fluid.algorithm import Algorithm
  from parl.core.fluid import layers
label_desc: Use image node_id 4 for calling the DQN obj's underlying member method code with example usage: obj.supervised_eval(obs, action, reward, next_obs, terminal) and returns: cost