docstr: Args:
      init_hidden_states (np.ndarray):       (batch_size, n_agents, rnn_hidden_dim)
      target_init_hidden_states (np.ndarray):(batch_size, n_agents, rnn_hidden_dim)
      state_batch (np.ndarray):              (batch_size, T, state_shape)
      actions_batch (np.ndarray):            (batch_size, T, n_agents)
      reward_batch (np.ndarray):             (batch_size, T, 1)
      terminated_batch (np.ndarray):         (batch_size, T, 1)
      obs_batch (np.ndarray):                (batch_size, T, n_agents, obs_shape)
      available_actions_batch (np.ndarray):  (batch_size, T, n_agents, n_actions)
      filled_batch (np.ndarray):             (batch_size, T, 1)
  Returns:
      mean_loss (float): train loss
      mean_td_error (float): train TD error
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code: def learn(
      self,
      state_batch,
      actions_batch,
      reward_batch,
      terminated_batch,
      obs_batch,
      available_actions_batch,
      filled_batch,
  ):
      """
      Args:
          init_hidden_states (np.ndarray):       (batch_size, n_agents, rnn_hidden_dim)
          target_init_hidden_states (np.ndarray):(batch_size, n_agents, rnn_hidden_dim)
          state_batch (np.ndarray):              (batch_size, T, state_shape)
          actions_batch (np.ndarray):            (batch_size, T, n_agents)
          reward_batch (np.ndarray):             (batch_size, T, 1)
          terminated_batch (np.ndarray):         (batch_size, T, 1)
          obs_batch (np.ndarray):                (batch_size, T, n_agents, obs_shape)
          available_actions_batch (np.ndarray):  (batch_size, T, n_agents, n_actions)
          filled_batch (np.ndarray):             (batch_size, T, 1)
      Returns:
          mean_loss (float): train loss
          mean_td_error (float): train TD error
      """
      (
          init_hidden_states,
          target_init_hidden_states,
      ) = self._get_hidden_states()
  
      if self.global_step % self.update_target_interval == 0:
          self.alg.sync_target()
          self.target_update_count += 1
  
      self.global_step += 1
  
      feed = {
          "init_hidden_states": init_hidden_states,
          "target_init_hidden_states": target_init_hidden_states,
          "state_batch": state_batch,
          "actions_batch": actions_batch,
          "reward_batch": reward_batch,
          "terminated_batch": terminated_batch,
          "obs_batch": obs_batch,
          "available_actions_batch": available_actions_batch,
          "filled_batch": filled_batch,
      }
      mean_loss, mean_td_error = self.fluid_executor.run(
          self.learn_program,
          feed=feed,
          fetch_list=[self.loss, self.mean_td_error],
      )
      mean_loss = mean_loss[0]
      mean_td_error = mean_td_error[0]
      return mean_loss, mean_td_error
  
file_path: PARL/benchmark/fluid/QMIX/qmix_agent.py
imports: import parl
  from parl import layers
  import paddle.fluid
  import numpy
  from utils import AvailableActionsSampler
  from parl.utils import machine_info
label_desc: Use image node_id 7 for calling the QMixAgent obj's underlying member method code with example usage: obj.learn(state_batch, actions_batch, reward_batch, terminated_batch, obs_batch, available_actions_batch, filled_batch) and returns: mean_loss,  mean_td_error