class_docstr: Base class for Hoeffding Decision Trees.
  
  This is an **abstract class**, so it cannot be used directly. It defines base operations
  and properties that all the Hoeffding decision trees must inherit or implement according to
  their own design.
  
  Parameters
  ----------
  max_depth
      The maximum depth a tree can reach. If `None`, the tree will grow indefinitely.
  binary_split
      If True, only allow binary splits.
  max_size
      The max size of the tree, in Megabytes (MB).
  memory_estimate_period
      Interval (number of processed instances) between memory consumption checks.
  stop_mem_management
      If True, stop growing as soon as memory limit is hit.
  remove_poor_attrs
      If True, disable poor attributes to reduce memory usage.
  merit_preprune
      If True, enable merit-based tree pre-pruning.
docstr: Print an explanation of how `x` is predicted.
  
  Parameters
  ----------
  x
      A dictionary of features.
  
  Returns
  -------
      A representation of the path followed by the tree to predict `x`; `None` if
      the tree is empty.
  
  Notes
  -----
  Currently, Label Combination Hoeffding Tree Classifier (for multi-label
  classification) is not supported.
code: def debug_one(self, x: dict) -> str | None:
      """Print an explanation of how `x` is predicted.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
          A representation of the path followed by the tree to predict `x`; `None` if
          the tree is empty.
  
      Notes
      -----
      Currently, Label Combination Hoeffding Tree Classifier (for multi-label
      classification) is not supported.
      """
      if self._root is None:
          return
  
      # We'll redirect all the print statement to a buffer, we'll return the content of the
      # buffer at the end
      buffer = io.StringIO()
      _print = functools.partial(print, file=buffer)
  
      for node in self._root.walk(x, until_leaf=True):
          if isinstance(node, HTLeaf):
              _print(repr(node))
          elif isinstance(node, DTBranch):
              try:
                  child_index = node.branch_no(x)
              except KeyError:
                  child_index, _ = node.most_common_path()
  
              _print(node.repr_branch(child_index))
  
      return buffer.getvalue()
  
imports: from __future__ import annotations
  import collections
  import functools
  import io
  import math
  from abc import ABC, abstractmethod
  from river import base
  from river.utils.norm import normalize_values_in_dict
  from .nodes.branch import DTBranch, NominalBinaryBranch, NominalMultiwayBranch, NumericBinaryBranch, NumericMultiwayBranch
  from .nodes.leaf import HTLeaf
  from .utils import calculate_object_size
label_desc: Use image node_id 23 for calling the HoeffdingTree obj's underlying member method code with example usage: obj.debug_one(x) and returns: buffer