docstr: input:  
      obs: observation, shape([B] + shape of obs_n[agent_index])
  output: 
      act: action, shape([B] + shape of act_n[agent_index])
docstr_tok: ["input",":","obs",":","observation",",","shape","(","[","B","]","+","shape","of","obs_n","[","agent_index","]",")","output",":","act",":","action",",","shape","(","[","B","]","+","shape","of","act_n","[","agent_index","]",")"]
code: def predict(self, obs):
      """input:
          obs: observation, shape([B] + shape of obs_n[agent_index])
      output:
          act: action, shape([B] + shape of act_n[agent_index])
      """
      this_policy = self.model.policy(obs)
      this_action = SoftPDistribution(
          logits=this_policy, act_space=self.act_space[self.agent_index]
      ).sample()
      return this_action
  
file_path: PARL/parl/algorithms/fluid/maddpg.py
imports: import warnings
  from parl.core.fluid import layers
  from copy import deepcopy
  from paddle import fluid
  from parl.core.fluid.algorithm import Algorithm
  from parl.core.fluid.policy_distribution import SoftCategoricalDistribution
  from parl.core.fluid.policy_distribution import SoftMultiCategoricalDistribution
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: this_action