code: def test_orc_keras():
      """Test case for ORCDataset with Keras"""
      orc_filename = os.path.join(
          os.path.dirname(os.path.abspath(__file__)),
          "test_orc",
          "iris.orc",
      )
  
      feature_cols = [
          "sepal_length",
          "sepal_width",
          "petal_length",
          "petal_width",
      ]
      label_cols = ["species"]
  
      feature_dataset = tfio.IODataset.from_orc(
          orc_filename, columns=feature_cols
      )
  
      label_dataset = tfio.IODataset.from_orc(
          orc_filename, columns=label_cols
      )
  
      @tf.function
      def species_float_conversion(x):
          if x == "virginica":
              return 1.0
          if x == "versicolor":
              return 2.0
          if x == "setosa":
              return 3.0
          return 4.0
  
      label_dataset = label_dataset.map(species_float_conversion)
      dataset = tf.data.Dataset.zip((feature_dataset, label_dataset))
      dataset = dataset.batch(1)
  
      def pack_features_vector(features, labels):
          """Pack the features into a single array."""
          features = tf.stack(list(features), axis=1)
          return features, labels
  
      dataset = dataset.map(pack_features_vector)
  
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(
                  10, activation=tf.nn.relu, input_shape=(4,)
              ),  # input shape required
              tf.keras.layers.Dense(10, activation=tf.nn.relu),
              tf.keras.layers.Dense(3),
          ]
      )
  
      model.compile(
          optimizer="adam",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      model.fit(dataset, epochs=5)
  
imports: import os
  import numpy
  import tensorflow
  import tensorflow_io
label_desc: Use image node_id 2 for calling a global function with example usage: test_orc_keras() and returns: int, features,  labels, int, int, int