code: def sample_batch(self, batch_size):
      batch = []
  
      if self.count < batch_size:
          batch = random.sample(self.buffer, self.count)
      else:
          batch = random.sample(self.buffer, batch_size)
      (
          s_batch,
          a_batch,
          r_batch,
          t_batch,
          obs_batch,
          available_actions_batch,
          filled_batch,
      ) = ([], [], [], [], [], [], [])
      for episode in batch:
          (
              s,
              a,
              r,
              t,
              obs,
              available_actions,
              filled,
          ) = episode.get_data()
          s_batch.append(s)
          a_batch.append(a)
          r_batch.append(r)
          t_batch.append(t)
          obs_batch.append(obs)
          available_actions_batch.append(available_actions)
          filled_batch.append(filled)
  
      s_batch = np.array(s_batch, dtype="float32")
      filled_batch = np.array(filled_batch, dtype="float32")
      r_batch = np.array(r_batch, dtype="float32")
      t_batch = np.array(t_batch, dtype="float32")
      a_batch = np.array(a_batch, dtype="long")
      obs_batch = np.array(obs_batch, dtype="float32")
      available_actions_batch = np.array(
          available_actions_batch, dtype="long"
      )
  
      return (
          s_batch,
          a_batch,
          r_batch,
          t_batch,
          obs_batch,
          available_actions_batch,
          filled_batch,
      )
  
file_path: PARL/benchmark/fluid/QMIX/replay_buffer.py
imports: from collections import deque
  import numpy
  import random
label_desc: Use image node_id 4 for calling the EpisodeReplayBuffer obj's underlying member method code with example usage: obj.sample_batch(batch_size) and returns: s_batch,  a_batch,  r_batch,  t_batch,  obs_batch,  available_actions_batch,  filled_batch