如何为可变长度序列制作掩码，然后在RNN的tensorflow2中填充这些掩码

import tensorflow as tf
from tensorflow.keras.layers import Embedding, LSTM, Dense, Masking
from tensorflow.keras.models import Sequential

# 示例数据
sequences = [
    [1, 2, 3, 0, 0],  # 长度为3
    [4, 5, 0, 0, 0],  # 长度为2
    [6, 7, 8, 9, 10]  # 长度为5
]
maxlen = 5

# 填充序列
padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=maxlen, padding='post')

# 创建掩码
mask = tf.cast(tf.not_equal(padded_sequences, 0), dtype=tf.float32)

# 构建模型
model = Sequential()
model.add(Embedding(input_dim=11, output_dim=32, input_length=maxlen))
model.add(Masking(mask_value=0.0))
model.add(LSTM(64))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 打印模型摘要
model.summary()

# 训练模型
model.fit(padded_sequences, tf.keras.utils.to_categorical([1, 0, 1]), epochs=5, batch_size=3)