def _variable_with_weight_decay(name, shape, stddev, wd):
    'Helper to create an initialized Variable with weight decay.\n\n  Note that the Variable is initialized with a truncated normal distribution.\n  A weight decay is added only if one is specified.\n\n  Args:\n    name: name of the variable\n    shape: list of ints\n    stddev: standard deviation of a truncated Gaussian\n    wd: add L2Loss weight decay multiplied by this float. If None, weight\n        decay is not added for this Variable.\n\n  Returns:\n    Variable Tensor\n  '
    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
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
def _variable_with_weight_decay(name, shape, stddev, wd):
    'Helper to create an initialized Variable with weight decay.\n\n  Note that the Variable is initialized with a truncated normal distribution.\n  A weight decay is added only if one is specified.\n\n  Args:\n    name: name of the variable\n    shape: list of ints\n    stddev: standard deviation of a truncated Gaussian\n    wd: add L2Loss weight decay multiplied by this float. If None, weight\n        decay is not added for this Variable.\n\n  Returns:\n    Variable Tensor\n  '
    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