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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None, num_readers=1):
 """Contruct batches of training or evaluation examples from the image dataset. Args: dataset: instance of Dataset class specifying the dataset. See dataset.py for details. batch_size: integer train: boolean num_preprocess_threads: integer, total number of preprocessing threads num_readers: integer, number of parallel readers Returns: images: 4-D float Tensor of a batch of images labels: 1-D integer Tensor of [batch_size]. Raises: ValueError: if data is not found """

  with tf.name_scope('batch_processing'):
    data_files = dataset.data_files()
    if data_files is None:
      raise ValueError('No data files found for this dataset')

    # Create filename_queue
    if train:
      filename_queue = tf.train.string_input_producer(data_files,
                                                      shuffle=True,
                                                      capacity=16)
    else:
      filename_queue = tf.train.string_input_producer(data_files,
                                                      shuffle=False,
                                                      capacity=1)
    if num_preprocess_threads is None:
      num_preprocess_threads = FLAGS.num_preprocess_threads

    if num_preprocess_threads % 4:
      raise ValueError('Please make num_preprocess_threads a multiple '
                       'of 4 (%d % 4 != 0).', num_preprocess_threads)

    if num_readers is None:
      num_readers = FLAGS.num_readers

    if num_readers < 1:
      raise ValueError('Please make num_readers at least 1')

    # Approximate number of examples per shard.
    examples_per_shard = 1024
    # Size the random shuffle queue to balance between good global
    # mixing (more examples) and memory use (fewer examples).
    # 1 image uses 299*299*3*4 bytes = 1MB
    # The default input_queue_memory_factor is 16 implying a shuffling queue
    # size: examples_per_shard * 16 * 1MB = 17.6GB
    min_queue_examples = examples_per_shard * FLAGS.input_queue_memory_factor
    if train:
      examples_queue = tf.RandomShuffleQueue(
          capacity=min_queue_examples + 3 * batch_size,
          min_after_dequeue=min_queue_examples,
          dtypes=[tf.string])
    else:
      examples_queue = tf.FIFOQueue(
          capacity=examples_per_shard + 3 * batch_size,
          dtypes=[tf.string])

    # Create multiple readers to populate the queue of examples.
    if num_readers > 1:
      enqueue_ops = []
      for _ in range(num_readers):
        reader = dataset.reader()
        _, value = reader.read(filename_queue)
        enqueue_ops.append(examples_queue.enqueue([value]))

      tf.train.queue_runner.add_queue_runner(
          tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))
      example_serialized = examples_queue.dequeue()
    else:
      reader = dataset.reader()
      _, example_serialized = reader.read(filename_queue)

    images_and_labels = []
    for thread_id in range(num_preprocess_threads):
      # Parse a serialized Example proto to extract the image and metadata.
      image_buffer, label_index, bbox, _ = parse_example_proto(
          example_serialized)
      image = image_preprocessing(image_buffer, bbox, train, thread_id)
      images_and_labels.append([image, label_index])

    images, label_index_batch = tf.train.batch_join(
        images_and_labels,
        batch_size=batch_size,
        capacity=2 * num_preprocess_threads * batch_size)

    # Reshape images into these desired dimensions.
    height = FLAGS.image_size
    width = FLAGS.image_size
    depth = 3

    images = tf.cast(images, tf.float32)
    images = tf.reshape(images, shape=[batch_size, height, width, depth])

    # Display the training images in the visualizer.
    tf.summary.image('images', images)

    return images, tf.reshape(label_index_batch, [batch_size])