message: Add examples for `tf.image.resize_with_crop_or_pad`
diff: @@ -1196,10 +1196,45 @@ def resize_image_with_crop_or_pad(image, target_height, target_width):
  If `width` or `height` is greater than the specified `target_width` or
  `target_height` respectively, this op centrally crops along that dimension.
  +
  + For example:
  +
  + >>> image = np.arange(75).reshape(5, 5, 3) # create 3-D image input
  + >>> image[:,:,0] # print first channel just for demo purposes
  + array([[ 0, 3, 6, 9, 12],
  + [15, 18, 21, 24, 27],
  + [30, 33, 36, 39, 42],
  + [45, 48, 51, 54, 57],
  + [60, 63, 66, 69, 72]])
  + >>> image = tf.image.resize_with_crop_or_pad(image, 3, 3) # crop
  + >>> # print first channel for demo purposes; centrally cropped output
  + >>> image[:,:,0]
  + <tf.Tensor: shape=(3, 3), dtype=int64, numpy=
  + array([[18, 21, 24],
  + [33, 36, 39],
  + [48, 51, 54]])>
  +
  If `width` or `height` is smaller than the specified `target_width` or
  `target_height` respectively, this op centrally pads with 0 along that
  dimension.
  + For example:
  +
  + >>> image = np.arange(1, 28).reshape(3, 3, 3) # create 3-D image input
  + >>> image[:,:,0] # print first channel just for demo purposes
  + array([[ 1, 4, 7],
  + [10, 13, 16],
  + [19, 22, 25]])
  + >>> image = tf.image.resize_with_crop_or_pad(image, 5, 5) # pad
  + >>> # print first channel for demo purposes; we should see 0 paddings
  + >>> image[:,:,0]
  + <tf.Tensor: shape=(5, 5), dtype=int64, numpy=
  + array([[ 0, 0, 0, 0, 0],
  + [ 0, 1, 4, 7, 0],
  + [ 0, 10, 13, 16, 0],
  + [ 0, 19, 22, 25, 0],
  + [ 0, 0, 0, 0, 0]])>
  +
  Args:
  image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
  of shape `[height, width, channels]`.
  