How to use the @tensorflow/tfjs.tensor1d function in @tensorflow/tfjs

wordExampleIndices.push(i);
      }
    }
    if (noiseExampleIndices.length === 0) {
      throw new Error(
          `Cannot perform augmentation by mixing with noise when ` +
          `there is no example with label ${BACKGROUND_NOISE_TAG}`);
    }

    const mixedXTensors: Array = [];
    const mixedLabelIndices: number[] = [];
    for (const index of wordExampleIndices) {
      const noiseIndex =  // Randomly sample from the noises, with replacement.
          noiseExampleIndices[getRandomInteger(0, noiseExampleIndices.length)];
      const signalTensor = isTypedArray ?
          tf.tensor1d(xs[index] as Float32Array) :
          xs[index] as tf.Tensor;
      const noiseTensor = isTypedArray ?
          tf.tensor1d(xs[noiseIndex] as Float32Array) :
          xs[noiseIndex] as tf.Tensor;
      const mixed: tf.Tensor =
          tf.tidy(() => normalize(signalTensor.add(noiseTensor.mul(ratio))));
      if (isTypedArray) {
        mixedXTensors.push(mixed.dataSync() as Float32Array);
      } else {
        mixedXTensors.push(mixed);
      }
      mixedLabelIndices.push(labelIndices[index]);
    }
    console.log(
        `Data augmentation: mixing noise: added ${mixedXTensors.length} ` +
        `examples`);

}
    if (key.includes("bn")) {
      // set gamma to scaled weights for odd layers
      let bnGammaShape = layers[key].getWeights()[0].shape;
      let index = parseInt(key.substring(2));
      let scaleWeightKey = "scale" + index;

      if (processedWeights[scaleWeightKey]) {
        w.push(tf.tensor1d(processedWeights[scaleWeightKey]['scale'], 'float32'));
      } else {
        w.push(tf.tensor1d(new Array(bnGammaShape[0]).fill(1), 'float32'));
      }

      // weight index 1 = beta - 0 (due to Affine = false)
      let bnBetaShape = layers[key].getWeights()[1].shape;
      w.push(tf.tensor1d(new Array(bnBetaShape[0]).fill(0), 'float32'));

      // weight indes 2 = moving_mean
      w.push(tf.tensor1d(processedWeights[key]['running_mean'], 'float32'));

      // weight index 3 = moving_variance
      w.push(tf.tensor1d(processedWeights[key]['running_var'], 'float32'));
    }
    if (key.includes("output")) {

      // weight index 0 = kernel
      let denseKernelShape = layers[key].getWeights()[0].shape;
      let denseKernel = matrix.transpose2d(processedWeights[key]['weight']);
      w.push(tf.tensor2d(denseKernel, denseKernelShape, 'float32'));

      // weight index 1 = bias
      w.push(tf.tensor1d(processedWeights[key]['bias'], 'float32'));

return tf.tidy(() => {
      const z = tf.randomNormal([1, this.nLatents], 0, 1, 'float32');
      // Get one hot for pitch encoding
      const pitchIdx = tf.tensor1d([pitch - this.minMidiPitch], 'int32');
      const pitchOneHot = tf.oneHot(pitchIdx, this.midiPitches);
      // Concat and add width and height dimensions.
      const cond = tf.concat([z, pitchOneHot], 1).expandDims(1).expandDims(1) as
          tf.Tensor4D;
      const specgrams = this.predict(cond, 1);
      return specgrams;
    });
  }

for (let i = 0; i < numExamples; ++i) {
    shuffledData.push(data[indices[i]]);
    shuffledTargets.push(targets[indices[i]]);
  }

  // Split the data into a training set and a tet set, based on `testSplit`.
  const numTestExamples = Math.round(numExamples * testSplit);
  const numTrainExamples = numExamples - numTestExamples;

  const xDims = shuffledData[0].length;

  const xs = tf.tensor2d(shuffledData, [numExamples, xDims]);

  // Create a 1D `tf.Tensor` to hold the labels, and convert the number label
  // from the set {0, 1, 2} into one-hot encoding (.e.g., 0 --> [1, 0, 0]).
  const ys = tf.oneHot(tf.tensor1d(shuffledTargets).toInt(), numClasses);

  const xTrain = xs.slice([0, 0], [numTrainExamples, xDims]);
  const xTest = xs.slice([numTrainExamples, 0], [numTestExamples, xDims]);
  const yTrain = ys.slice([0, 0], [numTrainExamples, numClasses]);
  const yTest = ys.slice([0, 0], [numTestExamples, numClasses]);
  return [xTrain, yTrain, xTest, yTest];
}

units: 1,
    }));
    model.compile({
        loss: "meanSquaredError",
        optimizer: "sgd",
    });

    const xArr = new Float32Array(6);
    let i = 0;
    [-1, 0, 1, 2, 3, 4].forEach(elem => {
        xArr[i++] = Number(elem);
    });

    const yArr = new Float32Array([-3, -1, 1, 3, 5, 7]);

    const xs = tf.tensor1d(xArr);
    const ys = tf.tensor1d(yArr);

    const epochs = 10;
    const h = await model.fit(xs, ys, {
        epochs
    });

    console.log("last loss:", h.history.loss[epochs - 1]);

    console.log("\nTEST: PREDICTING...");
    const out = model.predict(tf.tensor2d([10], [1, 1]));
    console.log(await out.data());
}

const slotTags: tf.Tensor3D = tf.tidy(() => {
                const y2sentences: tf.Tensor2D[] = [];
                for (const wordsSlotId of trainY2Chunks[index]) {
                    const slotIds = tf
                        .tensor1d(wordsSlotId, 'int32')
                        .pad([[0, this.datasetParams.maxWordsPerSentence - wordsSlotId.length]]);
                    const ohe = tf.oneHot(slotIds, slotsLength).asType('float32') as tf.Tensor2D;
                    slotIds.dispose();
                    y2sentences.push(ohe);
                }
                const stack = tf.stack(y2sentences) as tf.Tensor3D;
                y2sentences.forEach(s => s.dispose());
                return stack;
            });
            await this.model.fit([intentLabels, embeddedSentenceWords], slotTags, {

function imageTensorFromFlatArray(flat: number[], width: number, height: number) {
    return tf.transpose(tf.tensor1d(flat).reshape([1, height, width, 4]).slice([0, 0, 0, 0], [1, height, width, 3]), [0, 2, 1, 3])
}

return tf.tidy(() => {
    const player1Hand = tf.tensor1d(gameState.player1Hand, 'int32');
    const handOneHot = tf.oneHot(
        tf.sub(player1Hand, tf.scalar(1, 'int32')),
        game.GAME_STATE.max_card_value);
    const features = tf.sum(handOneHot, 0);
    const label = tf.tensor1d([gameState.player1Win]);
    return {xs: features, ys: label};
  });
}

export function padSequences(sequences, maxSeqLength) {
  let paddedSequences = []
  for (let sequence of sequences) {
    let t = tf.tensor1d(sequence).pad([[maxSeqLength - sequence.length, 0]])
    paddedSequences.push(t)
  }
  return tf.stack(paddedSequences)
}