class_docstr: Class to run multiple iterative algorithms and compare the results.
docstr: output the gradient norm per iteration for different methods
docstr_tok: ["output","the","gradient","norm","per","iteration","for","different","methods"]
code: def gradnorm_vals(self):
      """output the gradient norm per iteration for different methods"""
      gradnorm_dict = {}
      for params, name in zip(self.params, self.names):
          grad_norms = [
              np.linalg.norm(self.lrp.grad_wor(w)) for w in params
          ]
          gradnorm_dict[name] = [grad_norms]
      return gradnorm_dict
  
imports: import time
  from my_logistic_regression import MyLogisticRegression
  import numpy
label_desc: Use image node_id 7 for calling the CompareAlgs obj's underlying member method code with example usage: obj.gradnorm_vals() and returns: gradnorm_dict