code: def run_graph(
      wav_data,
      labels,
      input_layer_name,
      output_layer_name,
      num_top_predictions,
  ):
      """Runs the audio data through the graph and prints predictions."""
      with tf.compat.v1.Session() as sess:
          # Feed the audio data as input to the graph.
          #   predictions  will contain a two-dimensional array, where one
          #   dimension represents the input image count, and the other has
          #   predictions per class
          softmax_tensor = sess.graph.get_tensor_by_name(
              output_layer_name
          )
          (predictions,) = sess.run(
              softmax_tensor, {input_layer_name: wav_data}
          )
  
          # Sort to show labels in order of confidence
          top_k = predictions.argsort()[-num_top_predictions:][::-1]
          for node_id in top_k:
              human_string = labels[node_id]
              score = predictions[node_id]
              print("%s (score = %.5f)" % (human_string, score))
  
          return 0
  
file_path: tensorflow-upstream/tensorflow/examples/speech_commands/label_wav.py
imports: import argparse
  import sys
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: run_graph(wav_data, labels, input_layer_name, output_layer_name, num_top_predictions) and returns: int