code: def get_prediction(self, id):
      if self.is_classifier():
          # In a GBT model, the trees are always regressive trees (even if the GBT is a classifier). So we don't
          # have the probability attribute
          if (
              "tensorflow_decision_forests.keras.GradientBoostedTreesModel"
              in str(type(self.model))
          ):
              return self.tree_nodes[id].value.value
          return np.argmax(self.tree_nodes[id].value.probability)
      else:
          return self.tree_nodes[id].value.value
  
imports: from collections import defaultdict
  from typing import List, Mapping
  import numpy
  import tensorflow_decision_forests
  from tensorflow_decision_forests.component.py_tree.node import LeafNode
  from tensorflow_decision_forests.keras import RandomForestModel
  from tensorflow_decision_forests.tensorflow.core import Task
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree, VisualisationNotYetSupportedError
label_desc: Use image node_id 20 for calling the ShadowTensorflowTree obj's underlying member method code with example usage: obj.get_prediction(id) and returns: np, self, self