import tensorflow as tf

  """
  if weight_init is None:
    num_features = tensor.get_shape()[-1].value
    weight_init = tf.truncated_normal([num_features, size], stddev=0.01)
  if bias_init is None:
    bias_init = tf.zeros([size])

  with tf.name_scope(name, 'fully_connected', [tensor]):
    w = tf.Variable(weight_init, name='w', dtype=tf.float32)
    b = tf.Variable(bias_init, name='b', dtype=tf.float32)
