docstr: use the model to predict action
  
  Args:
      obs (torch tensor): observation, shape([batch_size] + obs_shape)
  Returns:
      action (torch tensor): action, shape([batch_size] + action_shape),
          noted that in the discrete case we take the argmax along the last axis as action
docstr_tok: ["use","the","model","to","predict","action","Args",":","obs","(","torch","tensor",")",":","observation",",","shape","(","[","batch_size","]","+","obs_shape",")","Returns",":","action","(","torch","tensor",")",":","action",",","shape","(","[","batch_size","]","+","action_shape",")",",","noted","that","in","the","discrete","case","we","take","the","argmax","along","the","last","axis","as","action"]
code: def predict(self, obs):
      """use the model to predict action
  
      Args:
          obs (torch tensor): observation, shape([batch_size] + obs_shape)
      Returns:
          action (torch tensor): action, shape([batch_size] + action_shape),
              noted that in the discrete case we take the argmax along the last axis as action
      """
      if self.continuous_action:
          action, _ = self.model.policy(obs)
      else:
          logits = self.model.policy(obs)
          probs = F.softmax(logits)
          action = paddle.argmax(probs, 1)
      return action
  
file_path: PARL/parl/algorithms/paddle/ppo.py
imports: import parl
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.optimizer
  from paddle.distribution import Normal, Categorical
  from parl.utils.utils import check_model_method
label_desc: Use image node_id 4 for calling the PPO obj's underlying member method code with example usage: obj.predict(obs) and returns: action