class_docstr: Dummy classifier which uses the prior distribution.
  
  The `predict_one` method will output the most common class whilst `predict_proba_one` will
  return the normalized class counts.
  
  Attributes
  ----------
  counts : collections.Counter
      Class counts.
  n : int
      Total number of seen instances.
  
  Examples
  --------
  
  Taken from example 2.1 from
  [this page](https://www.cms.waikato.ac.nz/~abifet/book/chapter_2.html)
  
  >>> from river import dummy
  
  >>> sentences = [
  ...     ('glad happy glad', '+'),
  ...     ('glad glad joyful', '+'),
  ...     ('glad pleasant', '+'),
  ...     ('miserable sad glad', '−')
  ... ]
  
  >>> model = dummy.PriorClassifier()
  
  >>> for sentence, label in sentences:
  ...     model.learn_one(sentence, label)
  
  >>> new_sentence = 'glad sad miserable pleasant glad'
  >>> model.predict_one(new_sentence)
  '+'
  >>> model.predict_proba_one(new_sentence)
  {'+': 0.75, '−': 0.25}
  
  References
  ----------
  [^1]: [Krichevsky–Trofimov estimator](https://www.wikiwand.com/en/Krichevsky%E2%80%93Trofimov_estimator)
code: def _multiclass(self):
      return True
  
imports: from __future__ import annotations
  import collections
  from river import base, stats
label_desc: Use image node_id 2 for calling the PriorClassifier obj's underlying member method code with example usage: obj._multiclass() and returns: True