docstr: use the policy model to predict actions
  
  Args:
      obs (paddle tensor): observation, shape([B] + shape of obs_n[agent_index])
  
  Returns:
      act (paddle tensor): action, shape([B] + shape of act_n[agent_index]),
          noted that in the discrete case we take the argmax along the last axis as action
docstr_tok: ["use","the","policy","model","to","predict","actions","Args",":","obs","(","paddle","tensor",")",":","observation",",","shape","(","[","B","]","+","shape","of","obs_n","[","agent_index","]",")","Returns",":","act","(","paddle","tensor",")",":","action",",","shape","(","[","B","]","+","shape","of","act_n","[","agent_index","]",")",",","noted","that","in","the","discrete","case","we","take","the","argmax","along","the","last","axis","as","action"]
code: def predict(self, obs):
      """use the policy model to predict actions
  
      Args:
          obs (paddle tensor): observation, shape([B] + shape of obs_n[agent_index])
  
      Returns:
          act (paddle tensor): action, shape([B] + shape of act_n[agent_index]),
              noted that in the discrete case we take the argmax along the last axis as action
      """
      policy = self.model.policy(obs)
      if self.continuous_actions:
          mean = policy[0]
          action = paddle.tanh(mean)
      else:
          action = F.softmax(policy, axis=-1)
      return action
  
file_path: PARL/parl/algorithms/paddle/maddpg.py
imports: import parl
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from parl.utils.utils import check_model_method
  from copy import deepcopy
label_desc: Use image node_id 2 for calling the MADDPG obj's underlying member method code with example usage: obj.predict(obs) and returns: action