code: def explain_prediction_path(
      tree_model,
      x: np.ndarray,
      X_train=None,
      y_train=None,  # required for XGBoost
      explanation_type: (
          "plain_english",
          "sklearn_default",
      ) = "plain_english",
      feature_names: List[str] = None,
      target_name: str = None,
      class_names: (
          Mapping[Number, str],
          List[str],
      ) = None,  # required if classifier,
      tree_index: int = None,  # required in case of tree ensemble
  ):
      """Prediction path interpretation for a data instance.
  
      In case explanation_type = 'plain_english', there will be created a range of values for each feature, based on data
      instance values and its tree prediction path.
      A possible output for this method could be :
          1.5 <= Pclass
          3.5 <= Age < 44.5
          7.91 <= Fare < 54.25
          0.5 <= Sex_label
          Cabin_label < 3.5
          0.5 <= Embarked_label
  
  
      :param tree_model: tree.DecisionTreeRegressor, tree.DecisionTreeClassifier, xgboost.core.Booster,
                  dtreeviz.models.sklearn_decision_trees.ShadowSKDTree,
                  dtreeviz.models.xgb_decision_trees.ShadowXGBDTree
          The tree model or dtreeviz shadow tree model to interpret
      :param x: np.ndarray
          The data instance for which we want to investigate prediction path
      :param y_train: pd.Series, np.ndarray
          Target variable values
      :param explanation_type: plain_english, sklearn_default
          Specify the interpretation type
      :param feature_names: List[str], optional
          The list of feature variable's name
      :param target_name: str, optional
          The name of target variable
      :param class_names: Mapping[Number, str], List[str], optional
          The list of class names. Required only for classifier
      :param tree_index: int, optional
          Required in case of tree ensemble. Specify the tree index to interpret.
  
      """
      shadow_tree = ShadowDecTree.get_shadow_tree(
          tree_model,
          X_train,
          y_train,
          feature_names,
          target_name,
          class_names,
          tree_index,
      )
      model = DTreeVizAPI(shadow_tree)
  
      if explanation_type == "sklearn_default":
          warnings.warn(
              "explain_prediction_path(explanation_type='sklearn_default') function is deprecated starting from version 2.0. \n "
              "For the same functionality, please use this code instead: \n m = dtreeviz.model(...) \n m.instance_feature_importance()",
              DeprecationWarning,
              stacklevel=2,
          )
          model.instance_feature_importance(x)
      else:
          warnings.warn(
              "explain_prediction_path() function is deprecated starting from version 2.0. \n "
              "For the same functionality, please use this code instead: \n m = dtreeviz.model(...) \n m.explain_prediction_path()",
              DeprecationWarning,
              stacklevel=2,
          )
          return model.explain_prediction_path(x)
  
imports: import warnings
  from numbers import Number
  from typing import Mapping, List
  import numpy
  import pandas
  from sklearn import tree
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree
  from dtreeviz.utils import myround, DTreeVizRender
  from dtreeviz.trees import DTreeVizAPI
label_desc: Use image node_id 13 for calling a global function with example usage: explain_prediction_path(tree_model, x, X_train, y_train, explanation_type, feature_names, target_name, class_names, tree_index) and returns: model