code: def best_split(tree, X, y):
      """Find the best split for a node."""
      # Need at least two elements to split a node.
      m = y.size
      if m <= 1:
          return None, None
      # Count of each class in the current node.
      num_parent = [np.sum(y == c) for c in range(tree.n_classes_)]
  
      # Gini of current node.
      best_gini = 1.0 - sum((n / m) ** 2 for n in num_parent)
      best_idx, best_thr = -1, best_gini
      if m > tree.feature_limit:
          split_futures = [
              best_split_for_idx_remote.remote(
                  tree, i, X, y, num_parent, best_gini
              )
              for i in range(tree.n_features_)
          ]
          best_splits = [ray.get(result) for result in split_futures]
      else:
          best_splits = [
              best_split_for_idx(tree, i, X, y, num_parent, best_gini)
              for i in range(tree.n_features_)
          ]
  
      ginis = np.array([x for (x, _) in best_splits])
      best_idx = np.argmin(ginis)
      best_thr = best_splits[best_idx][1]
  
      return best_idx, best_thr
  
imports: import numpy
  import ray
  from sklearn import datasets, metrics
  import time
  import tempfile
  import os
  import json
label_desc: Use image node_id 6 for calling a global function with example usage: best_split(tree, X, y) and returns: best_idx,  best_thr, None,  None