code: def execution_plan(
      workers: WorkerSet, config: AlgorithmConfig, **kwargs
  ) -> LocalIterator[dict]:
      assert (
          len(kwargs) == 0
      ), "MAML execution_plan does NOT take any additional parameters"
  
      # Sync workers with meta policy
      workers.sync_weights()
  
      # Samples and sets worker tasks
      use_meta_env = config.use_meta_env
      set_worker_tasks(workers, use_meta_env)
  
      # Metric Collector
      metric_collect = CollectMetrics(
          workers,
          min_history=config.metrics_num_episodes_for_smoothing,
          timeout_seconds=config.metrics_episode_collection_timeout_s,
      )
  
      # Iterator for Inner Adaptation Data gathering (from pre->post
      # adaptation)
      inner_steps = config.inner_adaptation_steps
  
      def inner_adaptation_steps(itr):
          buf = []
          split = []
          metrics = {}
          for samples in itr:
              # Processing Samples (Standardize Advantages)
              split_lst = []
              for sample in samples:
                  sample = convert_ma_batch_to_sample_batch(sample)
                  sample["advantages"] = standardized(
                      sample["advantages"]
                  )
                  split_lst.append(sample.count)
                  buf.append(sample)
  
              split.append(split_lst)
  
              adapt_iter = len(split) - 1
              metrics = post_process_metrics(
                  adapt_iter, workers, metrics
              )
              if len(split) > inner_steps:
                  out = concat_samples(buf)
                  out["split"] = np.array(split)
                  buf = []
                  split = []
  
                  # Reporting Adaptation Rew Diff
                  ep_rew_pre = metrics["episode_reward_mean"]
                  ep_rew_post = metrics[
                      "episode_reward_mean_adapt_" + str(inner_steps)
                  ]
                  metrics["adaptation_delta"] = ep_rew_post - ep_rew_pre
                  yield out, metrics
                  metrics = {}
              else:
                  inner_adaptation(workers, samples)
  
      rollouts = from_actors(workers.remote_workers())
      rollouts = rollouts.batch_across_shards()
      rollouts = rollouts.transform(inner_adaptation_steps)
  
      # Metaupdate Step
      train_op = rollouts.for_each(
          MetaUpdate(
              workers,
              config.maml_optimizer_steps,
              metric_collect,
              use_meta_env,
          )
      )
      return train_op
  
imports: import logging
  from typing import Optional, Type
  import numpy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.metrics import collect_metrics, get_learner_stats
  from ray.rllib.evaluation.worker_set import WorkerSet
  from ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, STEPS_TRAINED_COUNTER, STEPS_TRAINED_THIS_ITER_COUNTER, _get_shared_metrics
  from ray.rllib.execution.metric_ops import CollectMetrics
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import concat_samples, convert_ma_batch_to_sample_batch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
  from ray.rllib.utils.sgd import standardized
  from ray.util.iter import LocalIterator, from_actors
label_desc: Use image node_id 3 for calling the MAML obj's underlying member method code with example usage: obj.execution_plan(workers, config) and returns: train_op