docstr: Args:
      state (np.ndarray):                   (batch_size, T, state_shape)
      actions (np.ndarray):                 (batch_size, T, n_agents)
      reward (np.ndarray):                  (batch_size, T, 1)
      terminated (np.ndarray):              (batch_size, T, 1)
      obs (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
docstr_tok: ["Args",":","state","(","np.ndarray",")",":","(","batch_size",",","T",",","state_shape",")","actions","(","np.ndarray",")",":","(","batch_size",",","T",",","n_agents",")","reward","(","np.ndarray",")",":","(","batch_size",",","T",",","1",")","terminated","(","np.ndarray",")",":","(","batch_size",",","T",",","1",")","obs","(","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"]
code: def learn(
      self,
      state_batch,
      actions_batch,
      reward_batch,
      terminated_batch,
      obs_batch,
      available_actions_batch,
      filled_batch,
  ):
      """
      Args:
          state (np.ndarray):                   (batch_size, T, state_shape)
          actions (np.ndarray):                 (batch_size, T, n_agents)
          reward (np.ndarray):                  (batch_size, T, 1)
          terminated (np.ndarray):              (batch_size, T, 1)
          obs (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
      """
      if self.global_step % self.update_target_interval == 0:
          self.alg.sync_target()
          self.target_update_count += 1
  
      self.global_step += 1
  
      state_batch = paddle.to_tensor(state_batch, dtype="float32")
      actions_batch = paddle.to_tensor(actions_batch, dtype="int64")
      reward_batch = paddle.to_tensor(reward_batch, dtype="float32")
      terminated_batch = paddle.to_tensor(
          terminated_batch, dtype="float32"
      )
      obs_batch = paddle.to_tensor(obs_batch, dtype="float32")
      available_actions_batch = paddle.to_tensor(
          available_actions_batch, dtype="int64"
      )
      filled_batch = paddle.to_tensor(filled_batch, dtype="float32")
      mean_loss, mean_td_error = self.alg.learn(
          state_batch,
          actions_batch,
          reward_batch,
          terminated_batch,
          obs_batch,
          available_actions_batch,
          filled_batch,
      )
      return mean_loss, mean_td_error
  
file_path: PARL/examples/QMIX/qmix_agent.py
imports: import paddle
  import parl
  import numpy
  from utils import AvailableActionsSampler
  import os
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