docs: 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
    """
    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
    var = _variable_on_cpu(
        name,
        shape,
        tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
    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