class_docstr: Leaf that always predicts the majority class.
  
  Parameters
  ----------
  stats
      Initial class observations.
  depth
      The depth of the node.
  splitter
      The numeric attribute observer algorithm used to monitor target statistics
      and perform split attempts.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["Leaf","that","always","predicts","the","majority","class",".","Parameters","--","--","--","--","--","stats","Initial","class","observations",".","depth","The","depth","of","the","node",".","splitter","The","numeric","attribute","observer","algorithm","used","to","monitor","target","statistics","and","perform","split","attempts",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def prediction(self, x, *, tree=None):
      return normalize_values_in_dict(self.stats, inplace=False)
  
imports: from __future__ import annotations
  from river.tree.utils import BranchFactory
  from river.utils.norm import normalize_values_in_dict
  from ..splitter.nominal_splitter_classif import NominalSplitterClassif
  from ..utils import do_naive_bayes_prediction, round_sig_fig
  from .leaf import HTLeaf
label_desc: Use image node_id 4 for calling the LeafMajorityClass obj's underlying member method code with example usage: obj.prediction(x) and returns: normalize_values_in_dict