class_docstr: This class computes data balance measures for columns of interest
  based on a reference distribution. Currently, only the uniform reference
  distribution is supported.
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docstr: Returns distribution balance measures for the given dataset and
  columns of interest.
  
  The following measures are computed:
  * Kullback-Leibler Divergence -
      https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
  * Jensen-Shannon Distance -
      https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence
  * Wasserstein Distance -
      https://en.wikipedia.org/wiki/Wasserstein_metric
  * Infinity Norm Distance -
      https://en.wikipedia.org/wiki/Chebyshev_distance
  * Total Variation Distance -
      https://en.wikipedia.org/wiki/Total_variation_distance_of_probability_measures
  * Chi-Squared Test -
      https://en.wikipedia.org/wiki/Chi-squared_test
  * Cross Entropy -
      https://en.wikipedia.org/wiki/Cross_entropy
  
  :param dataset: The dataset to compute distribution measures on.
  :type dataset: pd.DataFrame
  :return: A pandas DataFrame that contains the given
      columns of interest and for each column of interest,
      a `dict` of <measure name, measure value> pairs.
  :rtype: pd.DataFrame
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code: def measures(self, dataset: pd.DataFrame) -> pd.DataFrame:
      """
      Returns distribution balance measures for the given dataset and
      columns of interest.
  
      The following measures are computed:
      * Kullback-Leibler Divergence -
          https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
      * Jensen-Shannon Distance -
          https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence
      * Wasserstein Distance -
          https://en.wikipedia.org/wiki/Wasserstein_metric
      * Infinity Norm Distance -
          https://en.wikipedia.org/wiki/Chebyshev_distance
      * Total Variation Distance -
          https://en.wikipedia.org/wiki/Total_variation_distance_of_probability_measures
      * Chi-Squared Test -
          https://en.wikipedia.org/wiki/Chi-squared_test
      * Cross Entropy -
          https://en.wikipedia.org/wiki/Cross_entropy
  
      :param dataset: The dataset to compute distribution measures on.
      :type dataset: pd.DataFrame
      :return: A pandas DataFrame that contains the given
          columns of interest and for each column of interest,
          a `dict` of <measure name, measure value> pairs.
      :rtype: pd.DataFrame
      """
      all_measures = [
          DistributionBalanceMeasures._get_distribution_measures_for_col(
              df=dataset, col_of_interest=col
          )
          for col in self.cols_of_interest
      ]
      return pd.DataFrame.from_dict(all_measures)
  
file_path: responsible-ai-toolbox/responsibleai/responsibleai/databalanceanalysis/distribution_balance_measures.py
imports: from typing import Callable, Dict, List
  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 2 for calling the DistributionBalanceMeasures obj's underlying member method code with example usage: obj.measures(dataset) and returns: pd