Tensorflow.js多输出LSTM模型出现问题。为什么说它没有连接？

Question

尝试使用tensorflow.js创建LSTM模型时遇到了一个问题，但我不断遇到此错误：

Uncaught (in promise) Error: Layer lstmLayer is not connected, no input to return.

我不确定它是怎么发生的。我尝试以几种不同的方式更改代码，但总是会回到该错误。我只是似乎无法弄清楚问题出在什么地方，在添加LSTM层之前，它工作得很好。我不是Tensorflow的javascript版本的佼佼者，因此任何指针都将大有帮助。我只是想让它工作，所以我可以继续对其进行更改，以更好地完成我想要的事情。

createModel() {
    return tf.tidy(() => {
        const inputLayer = tf.input({
            shape: [this.memoryLength, this.inputNodes],
            name: "inputLayer"
        });

        const lstmLayer = tf.layers.lstm({
            units: 8,
            returnSequences: true,
            name: "lstmLayer"
        }).apply(inputLayer);

        const hiddenLayer1 = tf.layers.dense({
            units: this.hiddenNodes1,
            activation: 'sigmoid',
            name: "hiddenLayer1"
        }).apply(lstmLayer);

        const dropout1 = tf.layers.dropout({
            rate: 0.2,
            name: "dropout1"
        }).apply(hiddenLayer1);

        const flattenLayer = tf.layers.flatten({
            name: "flattenLayer"
        }).apply(dropout1);

        const hiddenLayer2 = tf.layers.dense({
            units: this.hiddenNodes2,
            activation: 'sigmoid',
            name: "hiddenLayer2"
        }).apply(flattenLayer);

        const dropout2 = tf.layers.dropout({
            rate: 0.2,
            name: "dropout2"
        }).apply(hiddenLayer2);

        const hiddenLayer3 = tf.layers.dense({
            units: this.hiddenNodes3,
            activation: 'sigmoid',
            name: "hiddenLayer3"
        }).apply(flattenLayer);

        const dropout3 = tf.layers.dropout({
            rate: 0.2,
            name: "dropout3"
        }).apply(hiddenLayer3);

        const outputLayer1 = tf.layers.dense({
            units: this.inputNodes,
            activation: 'softmax',
            name: "outputLayer1"
        }).apply(dropout2);

        const outputLayer2 = tf.layers.dense({
            units: this.outputNodes,
            activation: 'softmax',
            name: "outputLayer2"
        }).apply(dropout3);

        const model = tf.model({
            inputs: [inputLayer],
            outputs: [outputLayer1, outputLayer2],
            name: "model"
        });

        model.summary();

        return model;
    });
}