code: def ensemble(
      experiment: str,
      config_path: str,
      data_path: str,
      evaluations_path: str,
      nskip: int,
  ):
      """
      Evaluates the performance of an ensemble of best-performing models. This
      allows also to only consider models of a particular type (thus building
      hyper-ensembles).
  
      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("__", content)
  
      for configuration in iterate_configurations(configs, nskip):
          run_sacred_script(
              "ensemble.py",
              experiment=experiment,
              data_path=data_path,
              evaluations_path=evaluations_path,
              **{k: v for k, v in configuration.items() if k != "__"},
          )
  
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: ensemble(experiment, config_path, data_path, evaluations_path, nskip) without return types