new_contents: """shell
  pip install -q -U autokeras==1.0.5
  pip install -q git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1
  """
  
  """
  Search for a good model for the
  [iris](https://www.tensorflow.org/datasets/catalog/iris) dataset.
  """
  
  import os
  
  import tensorflow as tf
  import pandas as pd
  
  import autokeras as ak
  
  # Prepare the dataset.
  train_dataset_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv"
  train_dataset_fp = tf.keras.utils.get_file(
      fname=os.path.basename(train_dataset_url),
      origin=train_dataset_url
  )
  
  test_dataset_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv"
  test_dataset_fp = tf.keras.utils.get_file(
      fname=os.path.basename(test_dataset_url),
      origin=test_dataset_url
  )
  
  column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
  feature_names = column_names[:-1]
  label_name = column_names[-1]
  class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica']
  
  train = pd.read_csv(
      train_dataset_fp,
      names=column_names,
      header=0
  )
  
  test = pd.read_csv(
      test_dataset_fp,
      names=column_names,
      header=0
  )
  
  print(train.shape)  # (120, 5)
  print(test.shape)  # (30, 5)
  
  # Initialize the StructuredDataClassifier.
  clf = ak.StructuredDataClassifier(
      max_trials=5,
      overwrite=True,
  )
  # Search for the best model with EarlyStopping.
  cbs = [
      tf.keras.callbacks.EarlyStopping(patience=3),
  ]
  
  clf.fit(
      x=train[feature_names],
      y=train[label_name],
      epochs=200,
      callbacks=cbs,
  )
  # Evaluate on the testing data.
  print('Accuracy: {accuracy}'.format(
      accuracy=clf.evaluate(x=test[feature_names], y=test[label_name])))
  
subject: Add example with iris dataset.