class_docstr: In the case of a crash, this class searches
  for the best individual model.
  
  Such model is returned as an ensemble of a single
  object, to comply with the expected interface of an
  AbstractEnsemble.
  
  Do not use by yourself!
class_docstr_tok: ["In","the","case","of","a","crash",",","this","class","searches","for","the","best","individual","model",".","Such","model","is","returned","as","an","ensemble","of","a","single","object",",","to","comply","with","the","expected","interface","of","an","AbstractEnsemble",".","Do","not","use","by","yourself","!"]
code: def __init__(
      self,
      task_type: int,
      metrics: Sequence[Scorer] | Scorer,
      backend: Backend,
      run_history: RunHistory,
      seed: int,
      random_state: int | np.random.RandomState | None = None,
  ):
      super().__init__(
          task_type=task_type,
          metrics=metrics,
          random_state=random_state,
          backend=backend,
      )
  
      # The seed here is seperate from RandomState and is used to indiicate a
      # directory for the backend to search in
      self.seed = seed
      self.indices_ = [0]
      self.weights_ = [1.0]
      self.run_history = run_history
      self.identifiers_ = self.get_identifiers_from_run_history()
  
imports: from __future__ import annotations
  from typing import Sequence
  import os
  import numpy
  from smac.runhistory.runhistory import RunHistory
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
label_desc: Use image node_id 1 to create a new SingleBestFromRunhistory object from inherited base classes: AbstractSingleModelEnsemble with example: obj = SingleBestFromRunhistory(task_type, metrics, backend, run_history, seed, random_state)