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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 _central_crop(image, crop_height, crop_width):
 """Performs central crops of the given image list. Args: image: a 3-D image tensor crop_height: the height of the image following the crop. crop_width: the width of the image following the crop. Returns: 3-D tensor with cropped image. """

  shape = tf.shape(image)
  height, width = shape[0], shape[1]

  mlperf_log.resnet_print(key=mlperf_log.INPUT_CENTRAL_CROP,
                          value=[crop_height, crop_width])

  amount_to_be_cropped_h = (height - crop_height)
  crop_top = amount_to_be_cropped_h // 2
  amount_to_be_cropped_w = (width - crop_width)
  crop_left = amount_to_be_cropped_w // 2
  return tf.slice(
      image, [crop_top, crop_left, 0], [crop_height, crop_width, -1])