How to use the datasets.dataset_utils.image_to_tfexample function in datasets

start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
            #sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
            #    i+1, len(filenames), shard_id))
            sys.stdout.write('>> Converting image %s \n' % (filenames[i]))
            sys.stdout.flush()

            # Read the filename:
            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
            height, width = image_reader.read_image_dims(sess, image_data)

            class_name = os.path.basename(os.path.dirname(filenames[i]))
            class_id = class_names_to_ids[class_name]

            example = dataset_utils.image_to_tfexample(
                image_data, b'jpg', height, width, class_id)
            tfrecord_writer.write(example.SerializeToString())

  sys.stdout.write('\n')
  sys.stdout.flush()

encoded_image = tf.image.encode_png(image_placeholder)

    with tf.Session('') as sess:

      for j in range(num_images):
        sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
            filename, offset + j + 1, offset + num_images))
        sys.stdout.flush()

        image = np.squeeze(images[j]).transpose((1, 2, 0))
        label = labels[j]

        png_string = sess.run(encoded_image,
                              feed_dict={image_placeholder: image})

        example = dataset_utils.image_to_tfexample(
            png_string, 'png', _IMAGE_SIZE, _IMAGE_SIZE, label, _CLASS_NAMES[label])
        tfrecord_writer.write(example.SerializeToString())

  return offset + num_images

with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                i+1, len(filenames), shard_id))
            sys.stdout.flush()

            # Read the filename:
            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
            height, width = image_reader.read_image_dims(sess, image_data)

            class_name = os.path.basename(os.path.dirname(filenames[i]))
            class_id = class_names_to_ids[class_name]

            example = dataset_utils.image_to_tfexample(
                image_data, b'jpg', height, width, class_id)
            tfrecord_writer.write(example.SerializeToString())

  sys.stdout.write('\n')
  sys.stdout.flush()

image_placeholder = tf.placeholder(dtype=tf.uint8)
        encoded_image = tf.image.encode_png(image_placeholder)

        with tf.Session('') as sess:
            for j in range(num_images):
                sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
                    filename, offset + j + 1, offset + num_images))
                sys.stdout.flush()

                image = np.squeeze(images[j]).transpose((1, 2, 0))
                label = labels[j]

                png_string = sess.run(encoded_image,
                                      feed_dict={image_placeholder: image})

                example = dataset_utils.image_to_tfexample(
                    png_string, b'png', _IMAGE_SIZE, _IMAGE_SIZE, label)
                tfrecord_writer.write(example.SerializeToString())

    return offset + num_images

with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                i+1, len(filenames), shard_id))
            sys.stdout.flush()
            # Read the filename:
            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
            height, width = image_reader.read_image_dims(sess, image_data)

            class_name = os.path.basename(os.path.dirname(filenames[i]))
            class_id = class_names_to_ids[class_name]

            example = dataset_utils.image_to_tfexample(
                image_data, b'jpg', height, width, class_id)
            tfrecord_writer.write(example.SerializeToString())

  sys.stdout.write('\n')
  sys.stdout.flush()

filename, offset + 1))
          sys.stdout.flush()

          if ('train' == name) and ( math.floor(offset / images_per_shard) > shard) :
            tfrecord_writer.close()
            shard = shard + 1
            record_filename = _get_output_filename(dataset_dir, name, shard, FLAGS.train_shards)
            tfrecord_writer = tf.python_io.TFRecordWriter(record_filename)

          image = np.squeeze(images[j]).transpose((1, 2, 0))
          label = labels[j]

          png_string = sess.run(encoded_image,
                                feed_dict={image_placeholder: image})

          example = dataset_utils.image_to_tfexample(
              png_string, 'png', _IMAGE_SIZE, _IMAGE_SIZE, label, _CLASS_NAMES[label])
          tfrecord_writer.write(example.SerializeToString())
          offset = offset + 1

  tfrecord_writer.close()
  return offset

with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                i+1, len(filenames), shard_id))
            sys.stdout.flush()

            # Read the filename:
            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
            height, width = image_reader.read_image_dims(sess, image_data)

            class_name = os.path.basename(os.path.dirname(filenames[i]))
            class_id = class_names_to_ids[class_name]

            example = dataset_utils.image_to_tfexample(
                image_data, b'jpg', height, width, class_id)
            tfrecord_writer.write(example.SerializeToString())

  sys.stdout.write('\n')
  sys.stdout.flush()

images = _extract_images(data_filename, num_images)
  labels = _extract_labels(labels_filename, num_images)

  shape = (_IMAGE_SIZE, _IMAGE_SIZE, _NUM_CHANNELS)
  with tf.Graph().as_default():
    image = tf.placeholder(dtype=tf.uint8, shape=shape)
    encoded_png = tf.image.encode_png(image)

    with tf.Session('') as sess:
      for j in range(num_images):
        sys.stdout.write('\r>> Converting image %d/%d' % (j + 1, num_images))
        sys.stdout.flush()

        png_string = sess.run(encoded_png, feed_dict={image: images[j]})

        example = dataset_utils.image_to_tfexample(
            png_string, 'png'.encode(), _IMAGE_SIZE, _IMAGE_SIZE, labels[j])
        tfrecord_writer.write(example.SerializeToString())

encoded_image = tf.image.encode_png(image_placeholder)

    with tf.Session('') as sess:

      for j in range(num_images):
        sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
            filename, offset + j + 1, offset + num_images))
        sys.stdout.flush()

        image = np.squeeze(images[j]).transpose((1, 2, 0))
        label = labels[j]

        png_string = sess.run(encoded_image,
                              feed_dict={image_placeholder: image})

        example = dataset_utils.image_to_tfexample(
            png_string, b'png', _IMAGE_SIZE, _IMAGE_SIZE, label)
        tfrecord_writer.write(example.SerializeToString())

  return offset + num_images