code: def _crop(image, offset_height, offset_width, crop_height, crop_width):
    
    original_shape = tf.shape(image)
  
    rank_assertion = tf.Assert(
        tf.equal(tf.rank(image), 3), ["Rank of image must be equal to 3."])
    with tf.control_dependencies([rank_assertion]):
      cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])
  
    size_assertion = tf.Assert(
        tf.logical_and(
            tf.greater_equal(original_shape[0], crop_height),
            tf.greater_equal(original_shape[1], crop_width)),
        ["Crop size greater than the image size."])
  
    offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
  
    # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
    # define the crop size.
    with tf.control_dependencies([size_assertion]):
      image = tf.slice(image, offsets, cropped_shape)
    return tf.reshape(image, cropped_shape)
docs: Crops the given image using the provided offsets and sizes.
  
    Note that the method doesn't assume we know the input image size but it does
    assume we know the input image rank.
  
    Args:
      image: `Tensor` image of shape [height, width, channels].
      offset_height: `Tensor` indicating the height offset.
      offset_width: `Tensor` indicating the width offset.
      crop_height: the height of the cropped image.
      crop_width: the width of the cropped image.
  
    Returns:
      the cropped (and resized) image.
  
    Raises:
      InvalidArgumentError: if the rank is not 3 or if the image dimensions are
        less than the crop size.