How to use the @tensorflow/tfjs-node-gpu.tidy function in @tensorflow/tfjs-node-gpu

const [noise, sampledLabels, trick] = tf.tidy(() => {
    // Make new latent vectors.
    const zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    const sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    // We want to train the generator to trick the discriminator.
    // For the generator, we want all the {fake, not-fake} labels to say
    // not-fake.
    const trick = tf.tidy(() => tf.ones([batchSize, 1]).mul(SOFT_ONE));
    return [zVectors, sampledLabels, trick];
  });

learn(experiences, gamma, tau=TAU) {    
    tf.tidy(() => {      
      const tensorified = {};
      Object.keys(experiences).map(function(key) {
        tensorified[key] = tf.tensor(experiences[key]);        
      });      
      const {states, actions, rewards, nextStates, dones} = tensorified;

      // Get predicted next-state actions and Q values from target models
      const actionsNext = this.actorTarget.predict(nextStates);
      const qTargetsNext = this.criticTarget.predict([nextStates, actionsNext]);        
      // Critic update
      this.criticOptimizer.minimize(() => {
        // Compute Q targets for current states (y-i)
        const qTargets = tf.add(rewards, tf.mul(tf.mul(gamma, qTargetsNext), tf.sub(1, dones)));
        const qExpected = this.critic.predict([states, actions]);
        const criticLoss = tf.losses.meanSquaredError(qExpected, qTargets);
        // torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1)

async function trainDiscriminatorOneStep(
    xTrain, yTrain, batchStart, batchSize, latentSize, generator,
    discriminator) {
  // TODO(cais): Remove tidy() once the current memory leak issue in tfjs-node
  //   and tfjs-node-gpu is fixed.
  const [x, y, auxY] = tf.tidy(() => {
    const imageBatch = xTrain.slice(batchStart, batchSize);
    const labelBatch = yTrain.slice(batchStart, batchSize).asType('float32');

    // Latent vectors.
    let zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    let sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    const generatedImages =
        generator.predict([zVectors, sampledLabels], {batchSize: batchSize});

    const x = tf.concat([imageBatch, generatedImages], 0);

    const y = tf.tidy(
        () => tf.concat(

async function trainCombinedModelOneStep(batchSize, latentSize, combined) {
  // TODO(cais): Remove tidy() once the current memory leak issue in tfjs-node
  //   and tfjs-node-gpu is fixed.
  const [noise, sampledLabels, trick] = tf.tidy(() => {
    // Make new latent vectors.
    const zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    const sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    // We want to train the generator to trick the discriminator.
    // For the generator, we want all the {fake, not-fake} labels to say
    // not-fake.
    const trick = tf.tidy(() => tf.ones([batchSize, 1]).mul(SOFT_ONE));
    return [zVectors, sampledLabels, trick];
  });

  const losses = await combined.trainOnBatch(
      [noise, sampledLabels], [trick, sampledLabels]);
  tf.dispose([noise, sampledLabels, trick]);

const [x, y, auxY] = tf.tidy(() => {
    const imageBatch = xTrain.slice(batchStart, batchSize);
    const labelBatch = yTrain.slice(batchStart, batchSize).asType('float32');

    // Latent vectors.
    let zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    let sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    const generatedImages =
        generator.predict([zVectors, sampledLabels], {batchSize: batchSize});

    const x = tf.concat([imageBatch, generatedImages], 0);

    const y = tf.tidy(
        () => tf.concat(
            [tf.ones([batchSize, 1]).mul(SOFT_ONE), tf.zeros([batchSize, 1])]));

    const auxY = tf.concat([labelBatch, sampledLabels], 0);
    return [x, y, auxY];
  });

async act (state, train=true) {
    const action = tf.tidy(() => {
      let action = tf.squeeze(this.actor.predict(tf.tensor([state])));
      if (train) {
        const noise = softmax(this.noise.sample());        
        action = action.mul(1-this.epsilon).add(tf.mul(noise, this.epsilon));
      }
      return action;
    });
    const data = await action.data();
    return Array.from(data);
  }