参考word_predict中的代码，从其中调用RNN，GRU，LSTM的类 实现单层RNN，GRU，LSTM的嵌套架构

在Python的`word_predict`代码中，通常会利用深度学习库如TensorFlow或PyTorch来实现RNN（循环神经网络），GRU（门控循环单元）和LSTM（长短时记忆网络）。下面是一个简化的示例，展示如何分别创建单层的RNN、GRU和LSTM模块：

import tensorflow as tf

# 单层RNN
class SimpleRNN(tf.keras.layers.Layer):
    def __init__(self, units, input_shape):
        super(SimpleRNN, self).__init__()
        self.rnn = tf.keras.layers.SimpleRNN(units, return_sequences=True)
        self.input_shape = input_shape

    def call(self, inputs):
        x = tf.reshape(inputs, (-1,) + self.input_shape) # Reshape input to match RNN's expectations
        output = self.rnn(x)
        return output

# 单层GRU
class SimpleGRU(tf.keras.layers.Layer):
    def __init__(self, units, input_shape):
        super(SimpleGRU, self).__init__()
        self.gru = tf.keras.layers.GRU(units, return_sequences=True)
        self.input_shape = input_shape

    def call(self, inputs):
        x = tf.reshape(inputs, (-1,) + self.input_shape)  # Similar reshape for GRU
        output = self.gru(x)
        return output

# 单层LSTM
class SimpleLSTM(tf.keras.layers.Layer):
    def __init__(self, units, input_shape):
        super(SimpleLSTM, self).__init__()
        self.lstm = tf.keras.layers.LSTM(units, return_sequences=True)
        self.input_shape = input_shape

    def call(self, inputs):
        x = tf.reshape(inputs, (-1,) + self.input_shape)  # LSTM also needs reshaping
        output = self.lstm(x)
        return output

在这段代码中，每个类都继承自`tf.keras.layers.Layer`，并设置了相应的RNN、GRU或LSTM层作为实例属性。`call`方法用于接收输入数据并通过这些层生成输出。