code: def main(argv):
      del argv
      neighbor_config = nsl.configs.GraphNeighborConfig(max_neighbors=3)
      train_dataset = gnn.make_cora_dataset(
          FLAGS.train_examples_path,
          shuffle=True,
          neighbor_config=neighbor_config,
      )
      eval_dataset = gnn.make_cora_dataset(
          FLAGS.eval_examples_path, batch_size=32
      )
  
      model = gnn.GraphConvolutionalNodeClassifier(
          seq_length=tf.data.experimental.get_structure(train_dataset)[
              0
          ]["words"].shape[-1],
          num_classes=7,
      )
      model.compile(
          optimizer=tf.keras.optimizers.Adam(),
          loss=tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True
          ),
          metrics=[
              tf.keras.metrics.SparseCategoricalCrossentropy(
                  from_logits=True
              ),
              tf.keras.metrics.SparseCategoricalAccuracy(),
              tf.keras.metrics.SparseTopKCategoricalAccuracy(2),
          ],
      )
      model.fit(train_dataset, epochs=30, validation_data=eval_dataset)
  
imports: from absl import app
  from absl import flags
  import graph_nets
  import neural_structured_learning
  from neural_structured_learning.experimental import gnn
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: main(argv) without return types