code: def _get_feature_columns():
      def _normalizer_fn(t):
          return tf.math.log1p(t * tf.sign(t)) * tf.sign(t)
  
      context_feature_columns = {
          "query_length": tf.feature_column.numeric_column(
              "query_length",
              shape=(1,),
              default_value=0,
              dtype=tf.int64,
          )
      }
      example_feature_columns = {
          "utility": tf.feature_column.numeric_column(
              "utility",
              shape=(1,),
              default_value=0.0,
              dtype=tf.float32,
              normalizer_fn=_normalizer_fn,
          ),
          "unigrams": tf.feature_column.embedding_column(
              tf.feature_column.categorical_column_with_vocabulary_list(
                  "unigrams",
                  vocabulary_list=[
                      "ranking",
                      "regression",
                      "classification",
                      "ordinal",
                  ],
              ),
              dimension=10,
          ),
      }
      custom_objects = {"_normalizer_fn": _normalizer_fn}
      return (
          context_feature_columns,
          example_feature_columns,
          custom_objects,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v2
  from tensorflow_ranking.python.keras import network
label_desc: Use image node_id 1 for calling a global function with example usage: _get_feature_columns() and returns: context_feature_columns,  example_feature_columns,  custom_objects, unknown