code: def _extract_outputs(element, root_graph, graph_name_to_output_names):
      outputs = [
          element[root_graph][_import_tensor_name(name)]
          for name in graph_name_to_output_names[root_graph]
      ]
      return outputs
  
file_path: tfx/tfx/experimental/distributed_inference/graphdef_experiments/subgraph_partitioning/beam_pipeline_test.py
imports: import os
  import tempfile
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import beam_pipeline
  from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import create_complex_graph
  from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import graph_partition
label_desc: Use image node_id 3 for calling a global function with example usage: _extract_outputs(element, root_graph, graph_name_to_output_names) and returns: outputs