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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def collect_samples(agents, sample_batch_size, num_envs_per_worker, train_batch_size):
 """Collects at least train_batch_size samples, never discarding any."""


    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)