code: def do_single_file_inference(input_file_path):
      with tfv1.Session(config=Config.session_config) as session:
          inputs, outputs, _ = create_inference_graph(
              batch_size=1, n_steps=-1
          )
  
          # Restore variables from training checkpoint
          load_graph_for_evaluation(session)
  
          features, features_len = audiofile_to_features(
              input_file_path
          )
          previous_state_c = np.zeros([1, Config.n_cell_dim])
          previous_state_h = np.zeros([1, Config.n_cell_dim])
  
          # Add batch dimension
          features = tf.expand_dims(features, 0)
          features_len = tf.expand_dims(features_len, 0)
  
          # Evaluate
          features = create_overlapping_windows(features).eval(
              session=session
          )
          features_len = features_len.eval(session=session)
  
          probs = outputs["outputs"].eval(
              feed_dict={
                  inputs["input"]: features,
                  inputs["input_lengths"]: features_len,
                  inputs["previous_state_c"]: previous_state_c,
                  inputs["previous_state_h"]: previous_state_h,
              },
              session=session,
          )
  
          probs = np.squeeze(probs)
  
          if FLAGS.scorer_path:
              scorer = Scorer(
                  FLAGS.lm_alpha,
                  FLAGS.lm_beta,
                  FLAGS.scorer_path,
                  Config.alphabet,
              )
          else:
              scorer = None
          decoded = ctc_beam_search_decoder(
              probs,
              Config.alphabet,
              FLAGS.beam_width,
              scorer=scorer,
              cutoff_prob=FLAGS.cutoff_prob,
              cutoff_top_n=FLAGS.cutoff_top_n,
          )
          # Print highest probability result
          print(decoded[0][1])
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import absl.app
  import numpy
  import progressbar
  import shutil
  import tensorflow
  import tensorflow.compat.v1
  import time
  from datetime import datetime
  from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
  from .evaluate import evaluate
  from six.moves import zip, range
  from .util.config import Config, initialize_globals
  from .util.checkpoints import load_or_init_graph_for_training, load_graph_for_evaluation, reload_best_checkpoint
  from .util.evaluate_tools import save_samples_json
  from .util.feeding import create_dataset, audio_to_features, audiofile_to_features
  from .util.flags import create_flags, FLAGS
  from .util.helpers import check_ctcdecoder_version, ExceptionBox
  from .util.logging import create_progressbar, log_debug, log_error, log_info, log_progress, log_warn
  from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path, isdir_remote
label_desc: Use image node_id 20 for calling a global function with example usage: do_single_file_inference(input_file_path) without return types