code: def _create_feature_spec() -> (
      Tuple[
          Dict[str, tf.io.FixedLenFeature],
          Dict[str, tf.io.FixedLenFeature],
          Tuple[str, tf.io.FixedLenFeature],
      ]
  ):
      """Create context and example feature spec for data parsing.
  
      Returns:
        (context feature specs, example feature specs, label spec).
      """
      context_feature_spec = {}
      example_feature_spec = {
          "custom_features_{}".format(i + 1): tf.io.FixedLenFeature(
              shape=(1,), dtype=tf.float32, default_value=0.0
          )
          for i in range(0, FLAGS.num_features)
      }
      label_spec = (
          _LABEL_FEATURE,
          tf.io.FixedLenFeature(
              shape=(1,), dtype=tf.float32, default_value=_PADDING_LABEL
          ),
      )
      return context_feature_spec, example_feature_spec, label_spec
  
file_path: ranking/tensorflow_ranking/examples/keras/keras_dnn_tfrecord.py
imports: from typing import Dict, Tuple
  from absl import app
  from absl import flags
  import tensorflow
  import tensorflow_ranking
label_desc: Use image node_id 1 for calling a global function with example usage: _create_feature_spec() and returns: context_feature_spec,  example_feature_spec,  label_spec