How to use the @tensorflow/tfjs.buffer function in @tensorflow/tfjs

const metadata = {
    'input_token_index': inputTokenIndex,
    'target_token_index': targetTokenIndex,
    'max_encoder_seq_length': maxEncoderSeqLength,
    'max_decoder_seq_length': maxDecoderSeqLength,
  };

  fs.writeFileSync(metadataJsonPath, JSON.stringify(metadata));
  console.log('Saved metadata at: ', metadataJsonPath);

  const encoderInputDataBuf = tf.buffer([
    inputTexts.length,
    maxEncoderSeqLength,
    numEncoderTokens,
  ]);
  const decoderInputDataBuf = tf.buffer([
    inputTexts.length,
    maxDecoderSeqLength,
    numDecoderTokens,
  ]);
  const decoderTargetDataBuf = tf.buffer([
    inputTexts.length,
    maxDecoderSeqLength,
    numDecoderTokens,
  ]);

  for (
    const [i, [inputText, targetText]]
    of (zip(inputTexts, targetTexts).entries() as IterableIterator<[number, [string, string]]>)
  ) {
    for (const [t, char] of inputText.split('').entries()) {
      // encoder_input_data[i, t, input_token_index[char]] = 1.

async function decodeSequence (
  inputSeq: tf.Tensor,
  encoderModel: tf.LayersModel,
  decoderModel: tf.LayersModel,
  numDecoderTokens: number,
  targetBeginIndex: number,
  reverseTargetCharIndex: {[indice: number]: string},
  maxDecoderSeqLength: number,
) {
  // Encode the input as state vectors.
  let statesValue = encoderModel.predict(inputSeq) as tf.Tensor[];

  // Generate empty target sequence of length 1.
  let targetSeq = tf.buffer([
    1,
    1,
    numDecoderTokens,
  ]);

  // Populate the first character of target sequence with the start character.
  targetSeq.set(1, 0, 0, targetBeginIndex);

  // Sampling loop for a batch of sequences
  // (to simplify, here we assume a batch of size 1).
  let stopCondition = false;
  let decodedSentence = '';
  while (!stopCondition) {
    const [outputTokens, h, c] = decoderModel.predict(
      [targetSeq.toTensor(), ...statesValue]
    ) as [

decodeSequence(inputSeq) {
    // Encode the inputs state vectors.
    let statesValue = this.encoderModel.predict(inputSeq);

    // Generate empty target sequence of length 1.
    let targetSeq = tf.buffer([1, 1, this.numDecoderTokens]);
    // Populate the first character of the target sequence with the start
    // character.
    targetSeq.set(1, 0, 0, this.targetTokenIndex['\t']);

    // Sample loop for a batch of sequences.
    // (to simplify, here we assume that a batch of size 1).
    let stopCondition = false;
    let decodedSentence = '';
    while (!stopCondition) {
      const predictOutputs =
          this.decoderModel.predict([targetSeq.toTensor()].concat(statesValue));
      const outputTokens = predictOutputs[0];
      const h = predictOutputs[1];
      const c = predictOutputs[2];

      // Sample a token.

this.state = this.zeroState;
    }

    const results = [];
    const userInput = Array.from(seed);
    const encodedInput = [];

    userInput.forEach((char) => {
      encodedInput.push(this.vocab[char]);
    });

    let input = encodedInput[0];
    let probabilitiesNormalized = []; // will contain final probabilities (normalized)

    for (let i = 0; i < userInput.length + length + -1; i += 1) {
      const onehotBuffer = tf.buffer([1, this.vocabSize]);
      onehotBuffer.set(1.0, 0, input);
      const onehot = onehotBuffer.toTensor();
      let output;
      if (this.model.embedding) {
        const embedded = tf.matMul(onehot, this.model.embedding);
        output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
      } else {
        output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
      }

      this.state.c = output[0];
      this.state.h = output[1];

      const outputH = this.state.h[1];
      const weightedResult = tf.matMul(outputH, this.model.fullyConnectedWeights);
      const logits = tf.add(weightedResult, this.model.fullyConnectedBiases);

encodeString(str) {
    const strLen = str.length;
    const encoded =
        tf.buffer([1, this.maxEncoderSeqLength, this.numEncoderTokens]);
    for (let i = 0; i < strLen; ++i) {
      if (i >= this.maxEncoderSeqLength) {
        console.error(
            'Input sentence exceeds maximum encoder sequence length: ' +
            this.maxEncoderSeqLength);
      }

      const tokenIndex = this.inputTokenIndex[str[i]];
      if (tokenIndex == null) {
        console.error(
            'Character not found in input token index: "' + tokenIndex + '"');
      }
      encoded.set(1, 0, i, tokenIndex);
    }
    return encoded.toTensor();
  }

if (!fs.existsSync(path.dirname(metadataJsonPath))) {
    mkdirp.sync(path.dirname(metadataJsonPath));
  }

  const metadata = {
    'input_token_index': inputTokenIndex,
    'target_token_index': targetTokenIndex,
    'max_encoder_seq_length': maxEncoderSeqLength,
    'max_decoder_seq_length': maxDecoderSeqLength,
  };

  fs.writeFileSync(metadataJsonPath, JSON.stringify(metadata));
  console.log('Saved metadata at: ', metadataJsonPath);

  const encoderInputDataBuf = tf.buffer([
    inputTexts.length,
    maxEncoderSeqLength,
    numEncoderTokens,
  ]);
  const decoderInputDataBuf = tf.buffer([
    inputTexts.length,
    maxDecoderSeqLength,
    numDecoderTokens,
  ]);
  const decoderTargetDataBuf = tf.buffer([
    inputTexts.length,
    maxDecoderSeqLength,
    numDecoderTokens,
  ]);

  for (

function discountRewards(rewards, discountRate) {
  const discountedBuffer = tf.buffer([rewards.length]);
  let prev = 0;
  for (let i = rewards.length - 1; i >= 0; --i) {
    const current = discountRate * prev + rewards[i];
    discountedBuffer.set(current, i);
    prev = current;
  }
  return discountedBuffer.toTensor();
}

export function encodeInputDateStrings(dateStrings) {
  const n = dateStrings.length;
  const x = tf.buffer([n, INPUT_LENGTH], 'float32');
  for (let i = 0; i < n; ++i) {
    for (let j = 0; j < INPUT_LENGTH; ++j) {
      if (j < dateStrings[i].length) {
        const char = dateStrings[i][j];
        const index = INPUT_VOCAB.indexOf(char);
        if (index === -1) {
          throw new Error(`Unknown char: ${char}`);
        }
        x.set(index, i, j);
      }
    }
  }
  return x.toTensor();
}

async feed(inputSeed, callback) {
    await this.ready;
    const seed = Array.from(inputSeed);
    const encodedInput = [];

    seed.forEach((char) => {
      encodedInput.push(this.vocab[char]);
    });

    let input = encodedInput[0];
    for (let i = 0; i < seed.length; i += 1) {
      const onehotBuffer = tf.buffer([1, this.vocabSize]);
      onehotBuffer.set(1.0, 0, input);
      const onehot = onehotBuffer.toTensor();
      let output;
      if (this.model.embedding) {
        const embedded = tf.matMul(onehot, this.model.embedding);
        output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
      } else {
        output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
      }
      this.state.c = output[0];
      this.state.h = output[1];
      input = encodedInput[i];
    }
    if (callback) {
      callback();
    }