code: def get_kr_correlation(
      p_pos: float,
      p_feature: float,
      p_pos_feature: float,
      total_count: int,
  ) -> float:
      """
      Returns the Kendall Rank Correlation of a feature, a correlation
      and statistical test that measures the ordinal association between two
      measured quantities.
      https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
  
      :param p_pos: The probability of the positive outcome in the dataset.
      :type p_pos: float
      :param p_feature: The probability of the feature in the dataset.
      :type p_feature: float
      :param p_pos_feature: The probability of the positive outcome and the
          feature in the dataset.
      :type p_pos_feature: float
      :param total_count: The total number of observations in the dataset.
      :type total_count: int
      :return: The Kendall Rank Correlation of the feature.
      :rtype: float
      """
      a = np.power(total_count, 2) * (
          1
          - 2 * p_feature
          - 2 * p_pos
          + 2 * p_pos_feature
          + 2 * p_pos * p_feature
      )
      b = total_count * (
          2 * p_feature + 2 * p_pos - 4 * p_pos_feature - 1
      )
      c = np.power(total_count, 2) * np.sqrt(
          (p_feature - np.power(p_feature, 2))
          * (p_pos - np.power(p_pos, 2))
      )
      return (a + b) / c
  
imports: import itertools
  import warnings
  from typing import Callable, Dict, List, Tuple
  import numpy
  import pandas
  import scipy
  from responsibleai.databalanceanalysis.balance_measures import BalanceMeasures
  from responsibleai.databalanceanalysis.constants import Constants, Measures
label_desc: Use image node_id 5 for calling a global function with example usage: get_kr_correlation(p_pos, p_feature, p_pos_feature, total_count) and returns: unknown