code: def _compute_distance(x1, x2, mean_vector, epsilon=0.0000001):
      """
      Compute distances between data points by using L1 on normalized data : sum(abs(x1-x2)/(mean_vector+epsilon))
  
      Parameters
      ----------
      x1 : array
          First vector
      x2 : array
          Second vector
      mean_vector : array
          Each value of this vector is the std.dev for each feature in dataset
  
      Returns
      -------
      diff : float
          Returns :math:`\\sum(\\frac{|x1-x2|}{mean\_vector+epsilon})`
      """
      diff = np.sum(np.abs(x1 - x2) / (mean_vector + epsilon))
      return diff
  
file_path: shapash/shapash/utils/explanation_metrics.py
imports: import numpy
  import pandas
  from sklearn.preprocessing import normalize
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_distance(x1, x2, mean_vector, epsilon) and returns: diff