class_docstr: Mondrian Tree classifier.
  
  Parameters
  ----------
  step
      Step of the tree.
  use_aggregation
      Whether to use aggregation weighting techniques or not.
  dirichlet
      Dirichlet parameter of the problem.
  split_pure
      Whether the tree should split pure leafs during training or not.
  iteration
      Number iterations to do during training.
  seed
      Random seed for reproducibility.
  
  Notes
  -----
  The Mondrian Tree Classifier is a type of decision tree that bases splitting decisions over a
  Mondrian process.
  
  Examples
  --------
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import metrics
  >>> from river import tree
  
  >>> dataset = datasets.Bananas().take(500)
  
  >>> model = tree.mondrian.MondrianTreeClassifier(
  ...     step=0.1,
  ...     use_aggregation=True,
  ...     dirichlet=0.2,
  ...     seed=1
  ... )
  
  >>> metric = metrics.Accuracy()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  Accuracy: 58.52%
  
  References
  ----------
  [^1]: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. Mondrian Forests: Efficient Online Random Forests.
      arXiv:1406.2673, pages 2-4
docstr: Compute the spit time of the given node.
  
  Parameters
  ----------
  node
      Target node.
docstr_tok: ["Compute","the","spit","time","of","the","given","node",".","Parameters","--","--","--","--","--","node","Target","node","."]
code: def _compute_split_time(
      self,
      y: base.typing.ClfTarget,
      node: MondrianLeafClassifier | MondrianBranchClassifier,
      extensions_sum: float,
  ) -> float:
      """Compute the spit time of the given node.
  
      Parameters
      ----------
      node
          Target node.
  
      """
  
      #  Don't split if the node is pure: all labels are equal to the one of y_t
      if not self.split_pure and node.is_dirac(y):
          return 0.0
  
      # If x_t extends the current range of the node
      if extensions_sum > 0:
          # Sample an exponential with intensity = extensions_sum
          T = utils.random.exponential(
              1 / extensions_sum, rng=self._rng
          )
  
          time = node.time
          # Splitting time of the node (if splitting occurs)
          split_time = time + T
          # If the node is a leaf we must split it
          if isinstance(node, MondrianLeafClassifier):
              return split_time
          # Otherwise we apply Mondrian process dark magic :)
          # 1. We get the creation time of the childs (left and right is the same)
          left, _ = node.children
          child_time = left.time
          # 2. We check if splitting time occurs before child creation time
          if split_time < child_time:
              return split_time
  
      return 0.0
  
imports: from __future__ import annotations
  import math
  from river import base, utils
  from river.tree.mondrian.mondrian_tree import MondrianTree
  from river.tree.mondrian.mondrian_tree_nodes import MondrianBranchClassifier, MondrianLeafClassifier, MondrianNodeClassifier
label_desc: Use image node_id 9 for calling the MondrianTreeClassifier obj's underlying member method code with example usage: obj._compute_split_time(y, node, extensions_sum) and returns: int, int, split_time, split_time