Applies sparse updates to individual values or slices within a given

variable according to indices.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref.

updates is Tensor of rank Q-1+P-K with shape:

$$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$

For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

    ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
    indices = tf.constant([[4], [3], [1] ,[7]])
    updates = tf.constant([9, 10, 11, 12])
    update = tf.scatter_nd_update(ref, indices, updates)
    with tf.Session() as sess:

The resulting update to ref would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

See also tf.scatter_update and tf.batch_scatter_update.