问LSTM注意力如何有可变长度的输入

EN

def attention(inputs, size, scope):
    with tf.variable_scope(scope or 'attention') as scope:
        attention_context_vector = tf.get_variable(name='attention_context_vector',
                                             shape=[size],
                                             regularizer=layers.l2_regularizer(scale=L2_REG),
                                             dtype=tf.float32)
        input_projection = layers.fully_connected(inputs, size,
                                            activation_fn=tf.tanh,
                                            weights_regularizer=layers.l2_regularizer(scale=L2_REG))
        vector_attn = tf.reduce_sum(tf.multiply(input_projection, attention_context_vector), axis=2, keep_dims=True)
        attention_weights = tf.nn.softmax(vector_attn, dim=1)
        weighted_projection = tf.multiply(inputs, attention_weights)
        outputs = tf.reduce_sum(weighted_projection, axis=1)

return outputs