class_docstr: Ensemble consisting of a single model.
  
  Parameters
  ----------
  task_type: int
      An identifier indicating which task is being performed.
  
  metrics: Sequence[Scorer] | Scorer
      The metrics used to evaluate the models.
  
  backend : Backend
      Gives access to the backend of Auto-sklearn. Not used.
  
  random_state: int | RandomState | None = None
      Not used.
class_docstr_tok: ["Ensemble","consisting","of","a","single","model",".","Parameters","--","--","--","--","--","task_type",":","int","An","identifier","indicating","which","task","is","being","performed",".","metrics",":","Sequence","[","Scorer","]","|","Scorer","The","metrics","used","to","evaluate","the","models",".","backend",":","Backend","Gives","access","to","the","backend","of","Auto-sklearn",".","Not","used",".","random_state",":","int","|","RandomState","|","None","=","None","Not","used","."]
docstr: Select the predictions of the selected model.
  
  Parameters
  ----------
  base_models_predictions : np.ndarray
      shape = (n_base_models, n_data_points, n_targets)
      Same as in the fit method.
  
  Returns
  -------
  np.ndarray
docstr_tok: ["Select","the","predictions","of","the","selected","model",".","Parameters","--","--","--","--","--","base_models_predictions",":","np.ndarray","shape","=","(","n_base_models",",","n_data_points",",","n_targets",")","Same","as","in","the","fit","method",".","Returns","--","--","--","-","np.ndarray"]
code: def predict(
      self, predictions: np.ndarray | list[np.ndarray]
  ) -> np.ndarray:
      """Select the predictions of the selected model.
  
      Parameters
      ----------
      base_models_predictions : np.ndarray
          shape = (n_base_models, n_data_points, n_targets)
          Same as in the fit method.
  
      Returns
      -------
      np.ndarray
      """
      return predictions[0]
  
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 3 for calling the AbstractSingleModelEnsemble obj's underlying member method code with example usage: obj.predict(predictions) and returns: predictions