How to use the @tensorflow/tfjs-layers.layers function in @tensorflow/tfjs-layers

async function exportMLPModel(exportPath) {
  const model = tfl.sequential();
  // Test both activations encapsulated in other layers and as standalone
  // layers.
  model.add(
      tfl.layers.dense({units: 100, inputShape: [200], activation: 'relu'}));
  model.add(tfl.layers.dense({units: 50, activation: 'elu'}));
  model.add(tfl.layers.dense({units: 24}));
  model.add(tfl.layers.activation({activation: 'elu'}));
  model.add(tfl.layers.dense({units: 8, activation: 'softmax'}));

  await saveModelAndRandomInputsAndOutputs(model, exportPath);
}

async function exportMLPModel(exportPath) {
  const model = tfl.sequential();
  // Test both activations encapsulated in other layers and as standalone
  // layers.
  model.add(
      tfl.layers.dense({units: 100, inputShape: [200], activation: 'relu'}));
  model.add(tfl.layers.dense({units: 50, activation: 'elu'}));
  model.add(tfl.layers.dense({units: 24}));
  model.add(tfl.layers.activation({activation: 'elu'}));
  model.add(tfl.layers.dense({units: 8, activation: 'softmax'}));

  await saveModelAndRandomInputsAndOutputs(model, exportPath);
}

async function exportCNNModel(exportPath) {
  const model = tfl.sequential();

  // Cover separable and non-separable convoluational layers.
  const inputShape = [40, 40, 3];
  model.add(tfl.layers.conv2d({
    filters: 32,
    kernelSize: [3, 3],
    strides: [2, 2],
    inputShape,
    padding: 'valid',
  }));
  model.add(tfl.layers.batchNormalization({}));
  model.add(tfl.layers.activation({activation: 'relu'}));
  model.add(tfl.layers.dropout({rate: 0.5}));
  model.add(tfl.layers.maxPooling2d({poolSize: 2}));
  model.add(tfl.layers.separableConv2d({
    filters: 32,
    kernelSize: [4, 4],
    strides: [3, 3],
  }));
  model.add(tfl.layers.batchNormalization({}));
  model.add(tfl.layers.activation({activation: 'relu'}));
  model.add(tfl.layers.dropout({rate: 0.5}));
  model.add(tfl.layers.avgPooling2d({poolSize: [2, 2]}));
  model.add(tfl.layers.flatten({}));
  model.add(tfl.layers.dense({units: 100, activation: 'softmax'}));

  await saveModelAndRandomInputsAndOutputs(model, exportPath);
}

async function exportCNNModel(exportPath) {
  const model = tfl.sequential();

  // Cover separable and non-separable convoluational layers.
  const inputShape = [40, 40, 3];
  model.add(tfl.layers.conv2d({
    filters: 32,
    kernelSize: [3, 3],
    strides: [2, 2],
    inputShape,
    padding: 'valid',
  }));
  model.add(tfl.layers.batchNormalization({}));
  model.add(tfl.layers.activation({activation: 'relu'}));
  model.add(tfl.layers.dropout({rate: 0.5}));
  model.add(tfl.layers.maxPooling2d({poolSize: 2}));
  model.add(tfl.layers.separableConv2d({
    filters: 32,
    kernelSize: [4, 4],
    strides: [3, 3],
  }));
  model.add(tfl.layers.batchNormalization({}));