async function evaluateModelOnTestData(model, testDataset) {
  ui.clearEvaluateTable();
  const [{xs: xTest, ys: yTest}] = await testDataset.toArray();
  const xData = xTest.dataSync();
  const yTrue = yTest.argMax(-1).dataSync();
  const predictOut = model.predict(xTest);
  const yPred = predictOut.argMax(-1);
  ui.renderEvaluateTable(xData, yTrue, yPred.dataSync(), predictOut.dataSync());
  calculateAndDrawConfusionMatrix(model, xTest, yTest);
  predictOnManualInput(model);
}
Run inference on some test Iris flower data.

@param model The instance of `tf.Model` to run the inference with.
@param testDataset A tf.Dataset object yielding features and targets. The
  features must be of shape [numTrainExamples, 4], while the targets must be
  [numTrainExamples, 3]. The four feature dimensions include the
  petal_length, petal_width, sepal_length and sepal_width.  The target is
  one-hot encoded labels of the three iris categories.