import tensorflow as tf

    # Calculate the total size of arguments on dimension 1.
    total_arg_size = 0
    shapes = [a.get_shape().as_list() for a in args]
    for shape in shapes:
        if len(shape) != 2:
            raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes))
        if not shape[1]:
            raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes))
        else:
            total_arg_size += shape[1]

    # Now the computation.
    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [total_arg_size, output_size])
        if len(args) == 1:
            res = tf.matmul(args[0], matrix)
        else:
            res = tf.matmul(tf.concat(values=args, axis=1), matrix)
        if not bias:
            return res
        bias_term = tf.get_variable("Bias", [output_size], initializer=tf.constant_initializer(bias_start))
        return res + bias_term

def _clip_and_normalize(word_probs, epsilon):
