class_docstr: The recommender analyzer evaluates the performance of a recommender across
  datasets.
  
  The analysis is run in parallel and should, thus, not be used in a Jupyter
  notebook. Instead, consider using the `tsbench` CLI.
class_docstr_tok: ["The","recommender","analyzer","evaluates","the","performance","of","a","recommender","across","datasets",".","The","analysis","is","run","in","parallel","and","should",",","thus",",","not","be","used","in","a","Jupyter","notebook",".","Instead",",","consider","using","the","`","tsbench","`","CLI","."]
docstr: Args:
      tracker: The tracker from which to obtain data and model performances.
      recommender: The recommender to use for obtaining models.
      num_recommendations: The number of recommendations to perform.
docstr_tok: ["Args",":","tracker",":","The","tracker","from","which","to","obtain","data","and","model","performances",".","recommender",":","The","recommender","to","use","for","obtaining","models",".","num_recommendations",":","The","number","of","recommendations","to","perform","."]
code: def __init__(
      self,
      tracker: ModelTracker,
      recommender: Recommender[ModelConfig],
      num_recommendations: int = 10,
  ):
      """
      Args:
          tracker: The tracker from which to obtain data and model performances.
          recommender: The recommender to use for obtaining models.
          num_recommendations: The number of recommendations to perform.
      """
      self.tracker = tracker
      self.recommender = recommender
      self.num_recommendations = num_recommendations
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/analysis/recommender/model.py
imports: from typing import Dict, List, Tuple
  from tsbench.analysis.utils import num_fitting_processes, run_parallel
  from tsbench.analysis.utils.loocv import loocv_split
  from tsbench.config import Config, ModelConfig
  from tsbench.evaluations.metrics import Performance
  from tsbench.evaluations.tracking import ModelTracker
  from tsbench.recommender import Recommender
label_desc: Use image node_id 1 to create a new ModelRecommenderAnalyzer object  with example: obj = ModelRecommenderAnalyzer(tracker, recommender, num_recommendations)