docstr: Predict the scores of all classes and output a `scores` dictionary
  with the new values.
  
  Parameters
  ----------
  dirichlet
      Dirichlet parameter of the tree.
  classes
      The set of classes seen so far
  n_classes
      The total number of classes of the problem.
docstr_tok: ["Predict","the","scores","of","all","classes","and","output","a","`","scores","`","dictionary","with","the","new","values",".","Parameters","--","--","--","--","--","dirichlet","Dirichlet","parameter","of","the","tree",".","classes","The","set","of","classes","seen","so","far","n_classes","The","total","number","of","classes","of","the","problem","."]
code: def predict(
      self, dirichlet: float, classes: set, n_classes: int
  ) -> dict[base.typing.ClfTarget, float]:
      """Predict the scores of all classes and output a `scores` dictionary
      with the new values.
  
      Parameters
      ----------
      dirichlet
          Dirichlet parameter of the tree.
      classes
          The set of classes seen so far
      n_classes
          The total number of classes of the problem.
  
      """
  
      scores = {}
      for c in classes:
          scores[c] = self.score(c, dirichlet, n_classes)
      return scores
  
imports: from __future__ import annotations
  import collections
  import math
  from river import base, stats
  from river.tree.base import Branch, Leaf
  from river.utils.math import log_sum_2_exp
label_desc: Use image node_id 4 for calling the MondrianNodeClassifier obj's underlying member method code with example usage: obj.predict(dirichlet, classes, n_classes) and returns: scores