docstr: min 1 / 2 / e^mu X + mu / 2
docstr_tok: ["min","1","\/","2","\/","e^mu","X","+","mu","\/","2"]
code: def log_std_weight(self, x, net, weight):
      """
      min 1 / 2 / e^mu X + mu / 2
      """
      mu_neg = net.NextScopedBlob("mu_neg")
      net.Negative(self.mu, mu_neg)
      mu_neg_exp = net.NextScopedBlob("mu_neg_exp")
      net.Exp(mu_neg, mu_neg_exp)
      net.Scale(mu_neg_exp, weight, scale=0.5)
  
file_path: pytorch/caffe2/python/layers/adaptive_weight.py
imports: import numpy
  from caffe2.python import core, schema
  from caffe2.python.layers.layers import ModelLayer
  from caffe2.python.regularizer import BoundedGradientProjection, LogBarrier
label_desc: Use image node_id 4 for calling the AdaptiveWeight obj's underlying member method code with example usage: obj.log_std_weight(x, net, weight) without return types