code: def __call__(self, data_tuple):
      # Metaupdate Step
      samples = data_tuple[0]
      adapt_metrics_dict = data_tuple[1]
  
      # Metric Updating
      metrics = _get_shared_metrics()
      metrics.counters[STEPS_SAMPLED_COUNTER] += samples.count
      fetches = None
      for i in range(self.maml_optimizer_steps):
          fetches = self.workers.local_worker().learn_on_batch(samples)
      learner_stats = get_learner_stats(fetches)
  
      # Sync workers with meta policy
      self.workers.sync_weights()
  
      # Set worker tasks
      set_worker_tasks(self.workers, self.use_meta_env)
  
      # Update KLS
      def update(pi, pi_id):
          assert "inner_kl" not in learner_stats, (
              "inner_kl should be nested under policy id key",
              learner_stats,
          )
          if pi_id in learner_stats:
              assert "inner_kl" in learner_stats[pi_id], (
                  learner_stats,
                  pi_id,
              )
              pi.update_kls(learner_stats[pi_id]["inner_kl"])
          else:
              logger.warning(
                  "No data for {}, not updating kl".format(pi_id)
              )
  
      self.workers.local_worker().foreach_policy_to_train(update)
  
      # Modify Reporting Metrics
      metrics = _get_shared_metrics()
      metrics.info[LEARNER_INFO] = fetches
      metrics.counters[STEPS_TRAINED_THIS_ITER_COUNTER] = samples.count
      metrics.counters[STEPS_TRAINED_COUNTER] += samples.count
  
      res = self.metric_gen.__call__(None)
      res.update(adapt_metrics_dict)
  
      return res
  
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 2 for calling the MetaUpdate obj's underlying member method code with example usage: obj.__call__(data_tuple) and returns: res