code: def make_cora_dataset(
      file_path,
      batch_size=128,
      shuffle=False,
      neighbor_config: Optional[configs.GraphNeighborConfig] = None,
      max_seq_length=1433,
      num_parallel_calls=tf.data.experimental.AUTOTUNE,
  ):
      """Returns a `tf.data.Dataset` instance based on data in `file_path`."""
      if neighbor_config is None:
          neighbor_config = configs.GraphNeighborConfig()
      features = {
          "words": tf.io.FixedLenFeature(
              [max_seq_length],
              tf.int64,
              default_value=tf.constant(
                  0, dtype=tf.int64, shape=[max_seq_length]
              ),
          ),
          "label": tf.io.FixedLenFeature(
              (), tf.int64, default_value=-1
          ),
      }
      for i in range(neighbor_config.max_neighbors):
          nbr_feature_key = "{}{}_{}".format(
              neighbor_config.prefix, i, "words"
          )
          nbr_weight_key = "{}{}{}".format(
              neighbor_config.prefix, i, neighbor_config.weight_suffix
          )
          features[nbr_feature_key] = tf.io.FixedLenFeature(
              [max_seq_length],
              tf.int64,
              default_value=tf.constant(
                  0, dtype=tf.int64, shape=[max_seq_length]
              ),
          )
          features[nbr_weight_key] = tf.io.FixedLenFeature(
              [1], tf.float32, default_value=tf.constant([0.0])
          )
      dataset = tf.data.experimental.make_batched_features_dataset(
          [file_path],
          batch_size,
          features,
          label_key="label",
          num_epochs=1,
          shuffle=shuffle,
          drop_final_batch=True,
      )
      dataset = dataset.map(
          functools.partial(
              pack_nodes_and_edges, batch_size, neighbor_config
          ),
          num_parallel_calls=num_parallel_calls,
      )
      return dataset.prefetch(num_parallel_calls)
  
imports: import functools
  from typing import Dict, Optional, Text
  import graph_nets
  import neural_structured_learning.configs
  import neural_structured_learning.keras
  import neural_structured_learning.lib
  import sonnet.v2
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: make_cora_dataset(file_path, batch_size, shuffle, neighbor_config, max_seq_length, num_parallel_calls) and returns: dataset