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: Raises a NotImplementedError.
  
  No need to call fit on the ``MajorityVoter``!
docstr_tok: ["Raises","a","NotImplementedError",".","No","need","to","call","fit","on","the","``","MajorityVoter","``","!"]
code: def fit(self, *args, **kwargs):
      """Raises a NotImplementedError.
  
      No need to call fit on the ``MajorityVoter``!
      """
      raise NotImplementedError(
          "No need to call fit on the 'MajorityVoter'!"
      )
  
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 2 for calling the MajorityVoter obj's underlying member method code with example usage: obj.fit() without return types