# tf.raw_ops.ScatterNdSub

Applies sparse subtraction to individual values or slices in a Variable.

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 `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 subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that subtraction 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.Session() as sess:
print sess.run(sub)
``````

The resulting update to ref would look like this:

``````[1, -9, 3, -6, -4, 6, 7, -4]
``````

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

`ref` A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. A mutable Tensor. Should be from a Variable node.
`indices` A `Tensor`. Must be one of the following types: `int32`, `int64`. 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 subtract from ref.
`use_locking` An optional `bool`. Defaults to `False`. 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).

A mutable `Tensor`. Has the same type as `ref`.