code: def cost_normalization(C, norm=None):
      
  
      if norm == "median":
          C /= float(np.median(C))
      elif norm == "max":
          C /= float(np.max(C))
      elif norm == "log":
          C = np.log(1 + C)
      elif norm == "loglog":
          C = np.log(1 + np.log(1 + C))
  
      return C
docs: Apply normalization to the loss matrix
  
  
      Parameters
      ----------
      C : np.array (n1, n2)
          The cost matrix to normalize.
      norm : str
          type of normalization from 'median','max','log','loglog'. Any other
          value do not normalize.
  
  
      Returns
      -------
  
      C : np.array (n1, n2)
          The input cost matrix normalized according to given norm.