class_docstr: Parameters:
      -----------
      nid : int, optional
          ID of the node.
      sitename : str, optional
          Name of the site that the node belongs to.
      fid : int, optional
          ID of the feature that the node splits on.
      bid : float or int, optional
          Feature value that the node splits on.
      weight : float, optional
          Weight of the node.
      is_leaf : bool, optional
          Boolean indicating whether the node is a leaf node.
      grad : float, optional
          Gradient value of the node.
      hess : float, optional
          Hessian value of the node.
      l : int, optional
          ID of the left child node.
      r : int, optional
          ID of the right child node.
      missing_dir : int, optional
          Direction for missing values (1 for left, -1 for right).
      sample_num : int, optional
          Number of samples in the node.
      is_left_node : bool, optional
          Boolean indicating whether the node is a left child node.
      sibling_nodeid : int, optional
          ID of the sibling node.
code: def to_dict(self):
      return {
          "nid": self.nid,
          "sitename": self.sitename,
          "fid": self.fid,
          "bid": self.bid,
          "weight": self.weight,
          "is_leaf": self.is_leaf,
          "grad": self.grad,
          "hess": self.hess,
          "l": self.l,
          "r": self.r,
          "missing_dir": self.missing_dir,
          "sample_num": self.sample_num,
          "is_left_node": self.is_left_node,
          "sibling_nodeid": self.sibling_nodeid,
          "parent_nodeid": self.parent_nodeid,
          "split_id": self.split_id,
      }
  
imports: import numpy
  import pandas
  from fate.arch import Context
  from fate.arch.dataframe import DataFrame
  from fate.ml.ensemble.learner.decision_tree.tree_core.splitter import SplitInfo
  from typing import List
  import logging
label_desc: Use image node_id 2 for calling the Node obj's underlying member method code with example usage: obj.to_dict() and returns: dict