docstr: Predict using the ensemble which is best for the 1st metric.
  
  Parameters
  ----------
  base_models_predictions : np.ndarray
      shape = (n_base_models, n_data_points, n_targets)
      Same as in the fit method.
  
  Returns
  -------
  np.ndarray
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code: def predict(
      self, base_models_predictions: np.ndarray | list[np.ndarray]
  ) -> np.ndarray:
      """Predict using the ensemble which is best for the 1st metric.
  
      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 self.pareto_set[0].predict(base_models_predictions)
  
imports: from __future__ import annotations
  from typing import Sequence
  import warnings
  import numpy
  from sklearn.exceptions import NotFittedError
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import TASK_TYPES
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble, AbstractMultiObjectiveEnsemble
  from autosklearn.ensembles.singlebest_ensemble import SingleModelEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.util.multiobjective import pareto_front
label_desc: Use image node_id 4 for calling the MultiObjectiveDummyEnsemble obj's underlying member method code with example usage: obj.predict(base_models_predictions) and returns: self