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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 parse_record(raw_record, is_training, dtype):
 """Parses a record containing a training example of an image. The input record is parsed into a label and image, and the image is passed through preprocessing steps (cropping, flipping, and so on). Args: raw_record: scalar Tensor tf.string containing a serialized Example protocol buffer. is_training: A boolean denoting whether the input is for training. dtype: data type to use for images/features. Returns: Tuple with processed image tensor and one-hot-encoded label tensor. """

  image_buffer, label = _parse_example_proto(raw_record)

  image = imagenet_preprocessing.preprocess_image(
      image_buffer=image_buffer,
      output_height=_DEFAULT_IMAGE_SIZE,
      output_width=_DEFAULT_IMAGE_SIZE,
      num_channels=_NUM_CHANNELS,
      is_training=is_training)
  image = tf.cast(image, dtype)

  return image, label