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# tf.compat.v1.scatter_nd_update

Applies sparse `updates` to individual values or slices in a Variable.

`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 `K`th 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])
with tf.compat.v1.Session() as sess:
print sess.run(update)
``````

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.

`ref` A Variable.
`indices` A `Tensor`. Must be one of the following types: `int32`, `int64`. A tensor of indices into ref.
`updates` A `Tensor`. Must have the same type as `ref`. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
`use_locking` An optional `bool`. Defaults to `True`. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
`name` A name for the operation (optional).

The value of the variable after the update.

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