code: def distance_variance(x):
      r"""Distance variance.
  
      Calculate the empirical distance variance as described in [1]_.
  
      Parameters
      ----------
      x : array_like, 1-D or 2-D
          If `x` is 1-D than it is assumed to be a vector of observations of a
          single random variable. If `x` is 2-D than the rows should be
          observations and the columns are treated as the components of a
          random vector, i.e., each column represents a different component of
          the random vector `x`.
  
      Returns
      -------
      float
          The empirical distance variance of `x`.
  
      References
      ----------
      .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007)
         "Measuring and testing dependence by correlation of distances".
         Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794.
  
      Examples
      --------
  
      >>> from statsmodels.stats.dist_dependence_measures import
      ... distance_variance
      >>> distance_variance(np.random.random(1000))
      0.21732609190659702
  
      """
      return distance_covariance(x, x)
  
file_path: statsmodels/statsmodels/stats/dist_dependence_measures.py
imports: from collections import namedtuple
  import warnings
  import numpy
  from scipy.spatial.distance import pdist, squareform
  from scipy.stats import norm
  from statsmodels.tools.sm_exceptions import HypothesisTestWarning
label_desc: Use image node_id 8 for calling a global function with example usage: distance_variance(x) and returns: distance_covariance