function getModel(labels) {
  const NUM_CLASSES = labels.length;
  const EMBEDDING_DIMS = 512;

  const model = tf.sequential();
  model.add(tf.layers.dense({
    inputShape: [EMBEDDING_DIMS],
    units: NUM_CLASSES,
    activation: 'softmax',
  }));

  model.compile({
    optimizer: 'adam',
    loss: 'categoricalCrossentropy',
    metrics: ['accuracy'],
  });

  return model;
}
Returns a new ready-to-train tf.Model that classifies 512 dimensional vectors
into one of labels.length categories.

The intended use is for the inputs to be embeddings from the universal
sentence encoder and labels to be the categories we want to classify those
sentence into.

@param {string[]} labels

@return {tf.Model} the model instance