code: def make_dataset(
      example_proto,
      input_shape,
      training,
      max_neighbors,
      include_key=False,
  ):
      """Construct a tf.data.Dataset from the given Example."""
  
      def make_parse_example_fn(feature_spec):
          def parse_example(serialized_example_proto):
              """Extracts relevant fields from the example_proto."""
              feature_dict = tf.io.parse_single_example(
                  serialized_example_proto, feature_spec
              )
              return feature_dict, feature_dict.pop(LABEL_NAME)
  
          return parse_example
  
      example = text_format.Parse(example_proto, tf.train.Example())
      serialized_example = example.SerializeToString()
      dataset = tf.data.Dataset.from_tensors(
          tf.convert_to_tensor(serialized_example)
      )
      if training:
          dataset = dataset.shuffle(10)
      dataset = dataset.map(
          make_parse_example_fn(
              make_feature_spec(input_shape, max_neighbors, include_key)
          )
      )
      dataset = dataset.batch(1)
      return dataset
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from neural_structured_learning import configs
  from research.carls import graph_regularization
  from research.carls import neighbor_cache_client
  import numpy
  import tensorflow
  from google.protobuf import text_format
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 5 for calling a global function with example usage: make_dataset(example_proto, input_shape, training, max_neighbors, include_key) and returns: dataset, parse_example, feature_dict,  feature_dict