Attempting to compute the gradient, both as part of an autographed keras layer, or in eager mode, fails. This seems to happen when concatenating different slices of a ragged tensor.

https://colab.research.google.com/drive/1kteIaQeDouRH-DEEYYeGE9jiMtgcXUZm#scrollTo=MOhPQNf4JDBB


import tensorflow as tf

values = tf.constant([0, 1,2,3,4,5,6,7,8,9], tf.float32)
values = tf.reshape(values, [-1, 1])

r = tf.RaggedTensor.from_row_lengths(values, [0, 2, 2, 1, 0, 2, 3, 0, 0])
r = tf.RaggedTensor.from_uniform_row_length(r, 3)
r = tf.RaggedTensor.from_uniform_row_length(r, 3)

def crop(raggedImage: tf.RaggedTensor, top, bottom, left, right) -> tf.RaggedTensor:
    '''
    Crops a ragged tensor, removing 'pixels' from the boundary.
    The input is interpreted as being b x h x w x s x c layout.
    Only the sample dimension may be ragged.
    '''
    if bottom > 0:
        bottom = -bottom
    else:
        bottom = None
    if right > 0:
        right = -right
    else:
        right = None
    cropped = raggedImage[:, top : bottom, left : right, :, :]
    return cropped

diameter = 3

with tf.GradientTape() as tape:
	tape.watch(r)
	l = []
	for u in range(diameter):
		for v in range(diameter):
			l.append(crop(r, u, diameter-1-u, v, diameter-1-v))
	s = tf.concat(l, axis=2)

tf.print(tape.gradient(s, [r]))

Traceback (most recent call last):
  File "venv-2.10/lib/python3.10/site-packages/tensorflow/python/eager/backprop.py", line 663, in _num_elements
    shape_tuple = grad.values._shape_tuple()  # pylint: disable=protected-access
AttributeError: 'IndexedSlices' object has no attribute '_shape_tuple'