code: def _example_feature_columns():
      return {
          "utility": tf.feature_column.numeric_column(
              "utility", shape=(1,), default_value=0.0, dtype=tf.float32
          ),
          "unigrams": tf.feature_column.embedding_column(
              tf.feature_column.categorical_column_with_vocabulary_list(
                  "unigrams",
                  vocabulary_list=[
                      "ranking",
                      "regression",
                      "classification",
                      "ordinal",
                  ],
              ),
              dimension=10,
          ),
      }
  
imports: from absl.testing import parameterized
  import tensorflow
  from google.protobuf import text_format
  from tensorflow_ranking.python import data
  from tensorflow_ranking.python.keras import feature
  from tensorflow_ranking.python.keras import losses
  from tensorflow_ranking.python.keras import metrics
  from tensorflow_ranking.python.keras import model
  from tensorflow_ranking.python.keras import network
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: _example_feature_columns() and returns: dict