code: def get_features(self) -> np.ndarray:
      if self.features is not None:
          return self.features
  
      feature_index = [self.__class__.NO_FEATURE] * len(self.tree_nodes)
      for index, node in self.tree_nodes.items():
          if hasattr(node, "condition"):
              feature_name = node.condition.feature.name
              feature_index[index] = self.feature_names.index(
                  feature_name
              )
  
      self.features = np.array(feature_index)
      return self.features
  
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 9 for calling the ShadowTensorflowTree obj's underlying member method code with example usage: obj.get_features() and returns: self, self