code: def _feature_grouping(self, param, net):
      # Possible alternative grouping method via summing over absolute values
      # Compute l2norm over feature weights
      # pow( sum_i { pow(theda_i, 2) } ,  0.5)
      param_mul = net.Mul(
          [param, param], [net.NextScopedBlob("param_mul")]
      )
      param_reduced = net.ReduceFrontSum(
          [param_mul], [net.NextScopedBlob("param_reduced")]
      )
      grouped_feature_weight_vec = net.Pow(
          [param_reduced],
          [net.NextScopedBlob("grouped_feature_weight_vec")],
          exponent=0.5,
      )
  
      return grouped_feature_weight_vec
  
imports: from caffe2.python import core, utils
  import numpy
label_desc: Use image node_id 5 for calling the Regularizer obj's underlying member method code with example usage: obj._feature_grouping(param, net) and returns: grouped_feature_weight_vec