code: def recommender(
      experiment: str,
      config_path: str,
      data_path: str,
      evaluations_path: str,
      nskip: int,
  ):
      """
      Evaluates the performance of the models proposed by a recommender on a
      hold-out dataset. Recommendations are made for each dataset by providing
      recommenders with the offline evaluations on the remaining datasets in the
      registry.
  
      This call runs the Sacred script for each provided configuration
      sequentially and returns only once all runs have completed.
      """
      with Path(config_path).open("r", encoding="utf-8") as f:
          content = yaml.safe_load(f)
          configs = explode_key_values("recommender", content)
  
      for configuration in iterate_configurations(configs, nskip):
          run_sacred_script(
              "recommender.py",
              experiment=experiment,
              data_path=data_path,
              evaluations_path=evaluations_path,
              **configuration,
          )
  
imports: from pathlib import Path
  import yaml
  from black import click
  from cli.utils import explode_key_values, iterate_configurations, run_sacred_script
  from tsbench.constants import DEFAULT_DATA_PATH, DEFAULT_EVALUATIONS_PATH
  from ._main import analysis
label_desc: Use image node_id 1 for calling a global function with example usage: recommender(experiment, config_path, data_path, evaluations_path, nskip) without return types