code: def imdb_raw(num_instances=100):
      dataset = tf.keras.utils.get_file(
          fname="aclImdb.tar.gz",
          origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
          extract=True,
      )
  
      # set path to dataset
      IMDB_DATADIR = os.path.join(os.path.dirname(dataset), "aclImdb")
  
      classes = ["pos", "neg"]
      train_data = load_files(
          os.path.join(IMDB_DATADIR, "train"),
          shuffle=True,
          categories=classes,
      )
      test_data = load_files(
          os.path.join(IMDB_DATADIR, "test"),
          shuffle=False,
          categories=classes,
      )
  
      x_train = np.array(train_data.data)
      y_train = np.array(train_data.target)
      x_test = np.array(test_data.data)
      y_test = np.array(test_data.target)
  
      if num_instances is not None:
          x_train = x_train[:num_instances]
          y_train = y_train[:num_instances]
          x_test = x_test[:num_instances]
          y_test = y_test[:num_instances]
      return (x_train, y_train), (x_test, y_test)
  
file_path: autokeras/benchmark/performance.py
imports: import os
  import numpy
  import tensorflow
  from sklearn.datasets import load_files
  from tensorflow.keras.datasets import cifar10
  from tensorflow.keras.datasets import mnist
  import autokeras
label_desc: Use image node_id 1 for calling a global function with example usage: imdb_raw(num_instances) and returns: x_train,  y_train,  x_test,  y_test