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.
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docstr: Fit the ensemble
  
  Parameters
  ----------
  base_models_predictions: np.ndarray
      shape = (n_base_models, n_data_points, n_targets)
      n_targets is the number of classes in case of classification,
      n_targets is 0 or 1 in case of regression
  
      Can be a list of 2d numpy arrays as well to prevent copying all
      predictions into a single, large numpy array.
  
  true_targets : array of shape [n_targets]
  
  model_identifiers : identifier for each base model.
      Can be used for practical text output of the ensemble.
  
  runs: Sequence[Run]
      Additional information for each run executed by SMAC that was
      considered by the ensemble builder.
  
  X_data : list-like | sparse matrix | None = None
  
  Returns
  -------
  self
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code: def fit(
      self,
      base_models_predictions: np.ndarray | list[np.ndarray],
      true_targets: np.ndarray,
      model_identifiers: list[tuple[int, int, float]],
      runs: Sequence[Run],
      X_data: SUPPORTED_FEAT_TYPES | None = None,
  ) -> AbstractSingleModelEnsemble:
      """Fit the ensemble
  
      Parameters
      ----------
      base_models_predictions: np.ndarray
          shape = (n_base_models, n_data_points, n_targets)
          n_targets is the number of classes in case of classification,
          n_targets is 0 or 1 in case of regression
  
          Can be a list of 2d numpy arrays as well to prevent copying all
          predictions into a single, large numpy array.
  
      true_targets : array of shape [n_targets]
  
      model_identifiers : identifier for each base model.
          Can be used for practical text output of the ensemble.
  
      runs: Sequence[Run]
          Additional information for each run executed by SMAC that was
          considered by the ensemble builder.
  
      X_data : list-like | sparse matrix | None = None
  
      Returns
      -------
      self
      """
      return self
  
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 2 for calling the AbstractSingleModelEnsemble obj's underlying member method code with example usage: obj.fit(base_models_predictions, true_targets, model_identifiers, runs, X_data) and returns: self