code: def set_worker_tasks(workers, use_meta_env):
      if use_meta_env:
          n_tasks = len(workers.remote_workers())
          tasks = (
              workers.local_worker()
              .foreach_env(lambda x: x)[0]
              .sample_tasks(n_tasks)
          )
          for i, worker in enumerate(workers.remote_workers()):
              worker.foreach_env.remote(
                  lambda env: env.set_task(tasks[i])
              )
  
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 1 for calling a global function with example usage: set_worker_tasks(workers, use_meta_env) without return types