code: def _variable_with_weight_decay(name, shape, stddev, wd):
      """Helper to create an initialized Variable with weight decay.
  
      Note that the Variable is initialized with a truncated normal distribution.
      A weight decay is added only if one is specified.
  
      Args:
        name: name of the variable
        shape: list of ints
        stddev: standard deviation of a truncated Gaussian
        wd: add L2Loss weight decay multiplied by this float. If None, weight
            decay is not added for this Variable.
  
      Returns:
        Variable Tensor
      """
      var = _variable_on_cpu(
          name, shape, tf.truncated_normal_initializer(stddev=stddev)
      )
      if wd is not None:
          weight_decay = tf.multiply(
              tf.nn.l2_loss(var), wd, name="weight_loss"
          )
          tf.add_to_collection("losses", weight_decay)
      return var
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from datetime import datetime
  import math
  import numpy
  from six.moves import xrange
  import tensorflow.compat.v1
  import time
  import utils
label_desc: Use image node_id 2 for calling a global function with example usage: _variable_with_weight_decay(name, shape, stddev, wd) and returns: var