code: def collect_samples(agents, sample_batch_size, num_envs_per_worker,
                      train_batch_size):
      
  
      num_timesteps_so_far = 0
      trajectories = []
      agent_dict = {}
  
      for agent in agents:
          fut_sample = agent.sample.remote()
          agent_dict[fut_sample] = agent
  
      while agent_dict:
          [fut_sample], _ = ray.wait(list(agent_dict))
          agent = agent_dict.pop(fut_sample)
          next_sample = ray_get_and_free(fut_sample)
          assert next_sample.count >= sample_batch_size * num_envs_per_worker
          num_timesteps_so_far += next_sample.count
          trajectories.append(next_sample)
  
          # Only launch more tasks if we don't already have enough pending
          pending = len(agent_dict) * sample_batch_size * num_envs_per_worker
          if num_timesteps_so_far + pending < train_batch_size:
              fut_sample2 = agent.sample.remote()
              agent_dict[fut_sample2] = agent
  
      return SampleBatch.concat_samples(trajectories)
docs: Collects at least train_batch_size samples, never discarding any.