code: def main(
      _seed: int,
      evaluations_path: str,
      recommender: str,
      num_recommendations: int,
      objectives: str,
      focus_objective: Optional[str],
      surrogate: Dict[str, Any],
  ):
      np.random.seed(_seed)
      torch.manual_seed(_seed)
      logging.getLogger("pytorch_lightning").setLevel(logging.WARNING)
  
      # First, get the tracker
      print("Fetching the data...")
      tracker = EnsembleTracker(Path(evaluations_path))
  
      # Then, potentially initialize the surrogate
      recommender_args: Dict[str, Any] = {
          "objectives": objectives.split(","),
          "focus": focus_objective,
          "recommends_ensembles": True,
      }
  
      print("Initializing the surrogate...")
      surrogate_metrics = [
          m
          for m in objectives.split(",")
          if (
              not m.startswith("latency")
              and not m.startswith("num_model_parameters")
          )
          or not surrogate["outputs"]["imputation"]
      ]
      recommender_args["surrogate"] = create_ensemble_surrogate(
          surrogate["name"],
          predict=surrogate_metrics,
          tracker=tracker,
          input_flags={},
          impute_simulatable=surrogate["outputs"]["imputation"],
          **(
              surrogate[surrogate["name"]]
              if surrogate["name"] in surrogate
              else {}
          ),
      )
  
      # Then, we can create the recommender
      print("Initializing the recommender...")
      recommender_instance = create_ensemble_recommender(
          recommender, **recommender_args
      )
  
      # And evaluate it
      print("Evaluating the recommender...")
      evaluator = EnsembleRecommenderAnalyzer(
          tracker,
          recommender_instance,
          num_recommendations=num_recommendations,
      )
      recommendations = evaluator.run()
  
      # Eventually, we store the results
      print("Storing the results...")
      with tempfile.TemporaryDirectory() as d:
          path = Path(d) / "recommendations.pickle"
          with path.open("wb+") as f:
              pickle.dump(recommendations, f)
          ex.add_artifact(path, content_type="application/octet-stream")
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble_recommender.py
imports: import logging
  import pickle
  import tempfile
  from pathlib import Path
  from typing import Any, Dict, Optional
  import numpy
  import torch
  from sacred import Experiment
  from tsbench.analysis.recommender import EnsembleRecommenderAnalyzer
  from tsbench.constants import DEFAULT_ENSEMBLE_EVALUATIONS_PATH
  from tsbench.evaluations.tracking import EnsembleTracker
  from tsbench.recommender import create_ensemble_recommender
  from tsbench.surrogate import create_ensemble_surrogate
label_desc: Use image node_id 2 for calling a global function with example usage: main(_seed, evaluations_path, recommender, num_recommendations, objectives, focus_objective, surrogate) without return types