docstr: `shap` library handling of `xgboost` causes a `ValueError` due to `xgboost` (features name mismatch)
  if you call `shap_interaction_values` with a `numpy` array (likely only if the user declares their
  `xgboost.DMatrix` object with the `feature_names` keyword argument). This method converts the
  incoming numpy array to an `xgboost.DMatrix` object with feature names that match the predictor.
code: def _xgboost_interactions(
      self, X: Union[np.ndarray, pd.DataFrame]
  ) -> Union[np.ndarray, List[np.ndarray]]:
      """
      `shap` library handling of `xgboost` causes a `ValueError` due to `xgboost` (features name mismatch)
      if you call `shap_interaction_values` with a `numpy` array (likely only if the user declares their
      `xgboost.DMatrix` object with the `feature_names` keyword argument). This method converts the
      incoming numpy array to an `xgboost.DMatrix` object with feature names that match the predictor.
      """
  
      import xgboost
  
      dexplain = xgboost.DMatrix(
          X, feature_names=self.predictor.feature_names
      )
      shap_output = self._explainer.shap_interaction_values(
          dexplain, tree_limit=self.tree_limit
      )
  
      return shap_output
  
imports: import copy
  import logging
  from functools import partial
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  import shap
  import shap.utils._legacy
  from scipy import sparse
  from scipy.special import expit
  from shap import KernelExplainer
  from alibi.api.defaults import DEFAULT_DATA_KERNEL_SHAP, DEFAULT_DATA_TREE_SHAP, DEFAULT_META_KERNEL_SHAP, DEFAULT_META_TREE_SHAP
  from alibi.api.interfaces import Explainer, Explanation, FitMixin
  from alibi.utils.wrappers import methdispatch
label_desc: Use image node_id 7 for calling the TreeShap obj's underlying member method code with example usage: obj._xgboost_interactions(X) and returns: shap_output