code: def export_structure(tree: DecisionTreeClassifier) -> tuple:
      tree = tree.tree_
  
      def recurse(node: int, depth: int) -> Iterator:
          feature_dim = tree.feature[node]
          if feature_dim == _tree.TREE_UNDEFINED:
              yield depth, -1, tree.value[node]
          else:
              threshold = tree.threshold[node]
              yield depth, feature_dim, threshold
              yield from recurse(tree.children_left[node], depth + 1)
              yield depth, feature_dim, threshold
              yield from recurse(tree.children_right[node], depth + 1)
  
      return tuple(recurse(0, 0))
  
imports: import torch
  import numpy
  import torch.nn.functional
  from typing import Dict
  from typing import List
  from typing import Iterator
  from typing import Optional
  from cftool.array import to_numpy
  from cftool.array import to_torch
  from .base import MLModel
  from ...schema import MLEncoderSettings
  from ...schema import MLGlobalEncoderSettings
  from ...modules.blocks import Linear
  from ...modules.blocks import Activation
label_desc: Use image node_id 1 for calling a global function with example usage: export_structure(tree) and returns: tuple