code: def _parallel_build_trees(X, y, tree, interval, lag, acf_min_values):
      """Private function used to fit a single tree in parallel."""
      temp_lag = lag
      if temp_lag > interval[1] - interval[0] - acf_min_values:
          temp_lag = interval[1] - interval[0] - acf_min_values
      if temp_lag < 0:
          temp_lag = 1
      temp_lag = int(temp_lag)
      transformed_x = _transform(X, interval, temp_lag)
      tree.fit(transformed_x, y)
  
      return temp_lag, tree
  
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: _parallel_build_trees(X, y, tree, interval, lag, acf_min_values) and returns: temp_lag,  tree