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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 _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle):
 """Construct a queued batch of images and labels. Args: image: 3-D Tensor of [height, width, 3] of type.float32. label: 1-D Tensor of type.int32 min_queue_examples: int32, minimum number of samples to retain in the queue that provides of batches of examples. batch_size: Number of images per batch. shuffle: boolean indicating whether to use a shuffling queue. Returns: images: Images. 4D tensor of [batch_size, height, width, 3] size. labels: Labels. 1D tensor of [batch_size] size. """

  # Create a queue that shuffles the examples, and then
  # read 'batch_size' images + labels from the example queue.
  num_preprocess_threads = 16
  if shuffle:
    images, label_batch = tf.train.shuffle_batch(
        [image, label],
        batch_size=batch_size,
        num_threads=num_preprocess_threads,
        capacity=min_queue_examples + 3 * batch_size,
        min_after_dequeue=min_queue_examples)
  else:
    images, label_batch = tf.train.batch(
        [image, label],
        batch_size=batch_size,
        num_threads=num_preprocess_threads,
        capacity=min_queue_examples + 3 * batch_size)

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

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