code: def _process_high_dim(
      arr: np.ndarray,
      radius: int,
      estimate_method: str,
      weights: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Processes high dimensional feature in the same way as 1-d feature."""
      kd_tree = sklearn.neighbors.KDTree(arr, metric="chebyshev")
      radius_ns = kd_tree.query_radius(X=arr, r=radius, count_only=True)
  
      if estimate_method == "smaller_data":
          each = -scipy.special.digamma(radius_ns) * weights
      elif estimate_method == "larger_data":
          each = -scipy.special.digamma(radius_ns - 1) * weights
      return np.sum(each), each
  
imports: import functools
  import itertools
  import math
  from typing import Any, List, Optional, Tuple, Union
  import uuid
  import numpy
  import pandas
  import scipy.special
  import sklearn.neighbors
label_desc: Use image node_id 15 for calling a global function with example usage: _process_high_dim(arr, radius, estimate_method, weights) and returns: np,  each