code: def _get_sparse_output_annotations_v1(
      graph: tf.compat.v1.Graph, session: Optional[tf.compat.v1.Session]
  ) -> Dict[Any, List[Union[str, tf.Tensor]]]:
      if not session:
          return {}
      else:
          return {
              tf_utils.hashable_tensor_or_op(kv[0]): session.run(kv[1])
              for kv in _get_sparse_output_annotations(graph)
          }
  
imports: import collections
  import itertools
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  from absl import logging
  import tensorflow
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_context
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.saved import saved_transform_io_v2
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx_bsl.tfxio import tensor_representation_util
  from google.protobuf import any_pb2
  from tensorflow.python.eager import function
  from tensorflow.python.framework import ops
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 24 for calling a global function with example usage: _get_sparse_output_annotations_v1(graph, session) and returns: dict, unknown