class_docstr: A basic label model that computes the majority vote across all rules.
  
  For single-label classification, it will predict the label with the most votes.
  For multi-label classification, it will predict all labels that got at least one vote by the rules.
  
  Args:
      weak_labels: The weak labels object.
class_docstr_tok: ["A","basic","label","model","that","computes","the","majority","vote","across","all","rules",".","For","single-label","classification",",","it","will","predict","the","label","with","the","most","votes",".","For","multi-label","classification",",","it","will","predict","all","labels","that","got","at","least","one","vote","by","the","rules",".","Args",":","weak_labels",":","The","weak","labels","object","."]
docstr: Helper method to compute scores for multi-label classifications.
  
  Args:
      probabilities: The probabilities.
  
  Returns:
      A tuple of the annotation and prediction array.
docstr_tok: ["Helper","method","to","compute","scores","for","multi-label","classifications",".","Args",":","probabilities",":","The","probabilities",".","Returns",":","A","tuple","of","the","annotation","and","prediction","array","."]
code: def _score_multi_label(
      self, probabilities: np.ndarray
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Helper method to compute scores for multi-label classifications.
  
      Args:
          probabilities: The probabilities.
  
      Returns:
          A tuple of the annotation and prediction array.
      """
      prediction = np.where(probabilities > 0.5, 1, 0)
  
      is_abstain = np.isnan(probabilities).all(axis=1)
  
      prediction, annotation = (
          prediction[~is_abstain],
          self._weak_labels.annotation()[~is_abstain],
      )
  
      return annotation, prediction
  
imports: import hashlib
  import logging
  from enum import Enum
  from typing import Dict, List, Tuple, Union
  import numpy
  from argilla import DatasetForTextClassification, TextClassificationRecord
  from argilla.labeling.text_classification.weak_labels import WeakLabels, WeakMultiLabels
  from argilla.utils.dependency import requires_dependencies
label_desc: Use image node_id 10 for calling the MajorityVoter obj's underlying member method code with example usage: obj._score_multi_label(probabilities) and returns: annotation,  prediction