code: def fit(
      self,
      predictions,
      labels,
      time_limit=None,
      identifiers=None,
      sample_weight=None,
  ):
      self.ensemble_size = int(self.ensemble_size)
      if self.ensemble_size < 1:
          raise ValueError("Ensemble size cannot be less than one!")
      if not self.problem_type in PROBLEM_TYPES:
          raise ValueError(
              "Unknown problem type %s." % self.problem_type
          )
      # if not isinstance(self.metric, Scorer):
      #     raise ValueError('Metric must be of type scorer')
  
      self._fit(
          predictions=predictions,
          labels=labels,
          time_limit=time_limit,
          sample_weight=sample_weight,
      )
      self._calculate_weights()
      logger.log(15, "Ensemble weights: ")
      logger.log(15, self.weights_)
      return self
  
imports: import logging
  import time
  from collections import Counter
  import numpy
  from ...constants import PROBLEM_TYPES
  from ...metrics import log_loss
  from ...utils import compute_weighted_metric, get_pred_from_proba
label_desc: Use image node_id 2 for calling the EnsembleSelection obj's underlying member method code with example usage: obj.fit(predictions, labels, time_limit, identifiers, sample_weight) and returns: self