code: def suggest_via_metalearning(
      meta_base,
      dataset_name,
      metric,
      task,
      sparse,
      num_initial_configurations,
      logger,
  ):
      if task == MULTILABEL_CLASSIFICATION:
          task = MULTICLASS_CLASSIFICATION
  
      task = TASK_TYPES_TO_STRING[task]
  
      logger.info(task)
  
      start = time.time()
      ml = MetaLearningOptimizer(
          dataset_name=dataset_name,
          configuration_space=meta_base.configuration_space,
          meta_base=meta_base,
          distance="l1",
          seed=1,
          logger=logger,
      )
      logger.info(
          "Reading meta-data took %5.2f seconds", time.time() - start
      )
      runs = ml.metalearning_suggest_all(
          exclude_double_configurations=True
      )
      return runs[:num_initial_configurations]
  
imports: import time
  from autosklearn.constants import MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, TASK_TYPES_TO_STRING
  from autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner import MetaLearningOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: suggest_via_metalearning(meta_base, dataset_name, metric, task, sparse, num_initial_configurations, logger) and returns: runs