code: def _fit(
      self,
      predictions: List[
          List[TimeSeriesDataFrame]
      ],  # first dim: model, second dim: val window index
      labels: List[TimeSeriesDataFrame],
      time_limit: Optional[int] = None,
      sample_weight=None,
  ):
      # Stack predictions for each model into a 3d tensor of shape [num_val_windows, num_rows, num_cols]
      stacked_predictions = [np.stack(preds) for preds in predictions]
  
      self.dummy_pred_per_window = []
      self.scorer_per_window = []
      self.data_future_per_window = []
  
      for window_idx, data in enumerate(labels):
          dummy_pred = copy.deepcopy(predictions[0][window_idx])
          # This should never happen; sanity check to make sure that all predictions have the same index
          assert all(
              dummy_pred.index.equals(pred[window_idx].index)
              for pred in predictions
          )
  
          self.dummy_pred_per_window.append(dummy_pred)
  
          scorer = copy.deepcopy(self.metric)
          # Split the observed time series once to avoid repeated computations inside the evaluator
          data_past = data.slice_by_timestep(
              None, -self.prediction_length
          )
          data_future = data.slice_by_timestep(
              -self.prediction_length, None
          )
          scorer.save_past_metrics(
              data_past,
              target=self.target,
              seasonal_period=self.eval_metric_seasonal_period,
          )
          self.scorer_per_window.append(scorer)
          self.data_future_per_window.append(data_future)
  
      super()._fit(
          predictions=stacked_predictions,
          labels=data_future,
          time_limit=time_limit,
      )
      self.dummy_pred_per_window = None
      self.evaluator_per_window = None
      self.data_future_per_window = None
  
imports: import copy
  import logging
  import pprint
  from typing import Dict, List, Optional
  import numpy
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.core.models.greedy_ensemble.ensemble_selection import EnsembleSelection
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.metrics import TimeSeriesScorer
  from autogluon.timeseries.models.ensemble import AbstractTimeSeriesEnsembleModel
  from autogluon.timeseries.utils.datetime import get_seasonality
label_desc: Use image node_id 2 for calling the TimeSeriesEnsembleSelection obj's underlying member method code with example usage: obj._fit(predictions, labels, time_limit, sample_weight) without return types