code: def main(_):
      """Convert a file to examples."""
      if FLAGS.subword_text_encoder_filename:
          encoder = text_encoder.SubwordTextEncoder(
              FLAGS.subword_text_encoder_filename
          )
      elif FLAGS.token_text_encoder_filename:
          encoder = text_encoder.TokenTextEncoder(
              FLAGS.token_text_encoder_filename
          )
      elif FLAGS.byte_text_encoder:
          encoder = text_encoder.ByteTextEncoder()
      else:
          encoder = None
      reader = tf.python_io.tf_record_iterator(FLAGS.input_filename)
      total_sequences = 0
      total_input_tokens = 0
      total_target_tokens = 0
      nonpadding_input_tokens = 0
      nonpadding_target_tokens = 0
      max_input_length = 0
      max_target_length = 0
      for record in reader:
          x = tf.train.Example()
          x.ParseFromString(record)
          inputs = [
              int(i)
              for i in x.features.feature["inputs"].int64_list.value
          ]
          targets = [
              int(i)
              for i in x.features.feature["targets"].int64_list.value
          ]
          if FLAGS.print_inputs:
              print(
                  "INPUTS:\n" + encoder.decode(inputs)
                  if encoder
                  else inputs
              )
          if FLAGS.print_targets:
              print(
                  "TARGETS:\n" + encoder.decode(targets)
                  if encoder
                  else targets
              )
          nonpadding_input_tokens += len(inputs) - inputs.count(0)
          nonpadding_target_tokens += len(targets) - targets.count(0)
          total_input_tokens += len(inputs)
          total_target_tokens += len(targets)
          total_sequences += 1
          max_input_length = max(max_input_length, len(inputs))
          max_target_length = max(max_target_length, len(targets))
          if FLAGS.print_all:
              for k, v in six.iteritems(x.features.feature):
                  print("%s: %s" % (k, v.int64_list.value))
  
      print("total_sequences: %d" % total_sequences)
      print("total_input_tokens: %d" % total_input_tokens)
      print("total_target_tokens: %d" % total_target_tokens)
      print("nonpadding_input_tokens: %d" % nonpadding_input_tokens)
      print("nonpadding_target_tokens: %d" % nonpadding_target_tokens)
      print("max_input_length: %d" % max_input_length)
      print("max_target_length: %d" % max_target_length)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  from tensor2tensor.data_generators import text_encoder
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) without return types