code: def _run_original_model(
      root_graph,
      root_graph_input_data,
      graph_name_to_filepath,
      graph_name_to_output_names,
  ):
      """Runs the original model."""
      graph_name_to_graph_def = (
          graph_partition.get_graph_name_to_graph_def(
              graph_name_to_filepath
          )
      )
      graph_def = graph_name_to_graph_def[root_graph]
      output_tensor_names = [
          _import_tensor_name(name)
          for name in graph_name_to_output_names[root_graph]
      ]
  
      outputs = []
      with tf.compat.v1.Session(graph=tf.Graph()) as sess:
          tf.import_graph_def(graph_def)
          for graph_name_to_feed_dict in root_graph_input_data:
              outputs.append(
                  sess.run(
                      output_tensor_names,
                      graph_name_to_feed_dict[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 1 for calling a global function with example usage: _run_original_model(root_graph, root_graph_input_data, graph_name_to_filepath, graph_name_to_output_names) and returns: outputs