code: def get_multivalent_features(
      schema: schema_pb2.Schema,
  ) -> Set[types.FeaturePath]:
      """Gets the set containing the names of all multivalent features.
  
      Args:
        schema: The schema for the data.
  
      Returns:
        A set containing the names of all multivalent features.
      """
  
      # Check if the feature is not univalent. A univalent feature will either
      # have the shape field set with one dimension of size 1 or the value_count
      # field set with a max value_count of 1.
      # pylint: disable=g-complex-comprehension
      return {
          feature_path
          for feature_path, feature in get_all_leaf_features(schema)
          if not (
              (
                  feature.shape
                  and feature.shape.dim
                  and len(feature.shape.dim)
                  == feature.shape.dim[0].size
                  == 1
              )
              or (feature.value_count and feature.value_count.max == 1)
          )
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import logging
  from typing import Any, Iterable, List, Mapping, Optional, Set, Text, Tuple, Union
  from tensorflow_data_validation import types
  from tensorflow_data_validation.utils import io_util
  from google.protobuf import descriptor
  from google.protobuf import text_format
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 11 for calling a global function with example usage: get_multivalent_features(schema) and returns: unknown