class_docstr: Fit and predict one or more estimators on one or more datasets.
class_docstr_tok: ["Fit","and","predict","one","or","more","estimators","on","one","or","more","datasets","."]
code: def __init__(self, tasks, datasets, strategies, cv, results):
      # validate datasets and tasks
      self._validate_tasks_and_datasets(tasks, datasets)
      self.tasks = tasks
      self.datasets = datasets
  
      # validate strategies
      self._validate_strategy_names(strategies)
      self.strategies = strategies
  
      self.cv = cv
      self.results = results
  
      # attach cv iterator to results object
      self.results.cv = cv
  
      # progress trackers
      self.n_strategies = len(strategies)
      self.n_datasets = len(datasets)
      self._strategy_counter = 0
      self._dataset_counter = 0
  
file_path: sktime/sktime/benchmarking/orchestration.py
imports: import logging
  import pandas
  from sklearn.base import clone
  from sktime.benchmarking.tasks import TSCTask, TSRTask
label_desc: Use image node_id 1 to create a new Orchestrator object  with example: obj = Orchestrator(tasks, datasets, strategies, cv, results)