class_docstr: Regularization for Elastic Net Regression 
class_docstr_tok: ["Regularization","for","Elastic","Net","Regression"]
code: def grad(self, w):
      l1_contr = self.l1_ratio * np.sign(w)
      l2_contr = (1 - self.l1_ratio) * w
      return self.alpha * (l1_contr + l2_contr)
  
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import normalize, polynomial_features
label_desc: Use image node_id 3 for calling the l1_l2_regularization obj's underlying member method code with example usage: obj.grad(w) and returns: unknown