code: def _mask(self, weights):
          
          mask = (np.abs(weights) >= np.sqrt(EPS)) * np.isfinite(weights)
          if mask.sum() == 0:
              raise OptimizationError('PIRLS optimization has diverged.\n' +
                  'Try increasing regularization, or specifying an initial value for self.coef_')
          return mask
docs: identifies the mask at which the weights are
              greater than sqrt(machine epsilon)
          and
              not NaN
          and
              not Inf
  
  
          Parameters
          ---------
          weights : array-like of shape (n,)
              containing weights in [0,1]
  
          Returns
          -------
          mask : boolean np.array of shape (n,) of good weight values