# tensorflow::ops::ScatterNd

`#include <array_ops.h>`

Creates a new tensor by applying sparse `updates` to individual.

## Summary

values or slices within a zero tensor of the given `shape` tensor according to indices. This operator is the inverse of the tf.gather_nd operator which extracts values or slices from a given tensor.

`shape` is a `TensorShape` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `shape`. 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 `shape`.

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

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

The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

In Python, this scatter operation would look like this:

```indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
shape = tf.constant([8])
with tf.Session() as sess:
print sess.run(scatter)
```

The resulting tensor would look like this:

```[0, 11, 0, 10, 9, 0, 0, 12]
```

We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.

In Python, this scatter operation would look like this:

```indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
shape = tf.constant([4, 4, 4])
with tf.Session() as sess:
print sess.run(scatter)
```

The resulting tensor would look like this:

```[[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
```

Arguments:

• scope: A Scope object
• 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 tensor. A tensor of updated values to store in ref.
• shape: A vector. The shape of the resulting tensor.

Returns:

• `Output`: A new tensor with the given shape and updates applied according to the indices.

### Constructors and Destructors

`ScatterNd(const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input updates, ::tensorflow::Input shape)`

### Public attributes

`output`
`::tensorflow::Output`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
``` ```
``` ```
`operator::tensorflow::Output() const `
``` ```
``` ```

## Public attributes

### output

`::tensorflow::Output output`

## Public functions

### ScatterNd

``` ScatterNd(
const ::tensorflow::Scope & scope,
::tensorflow::Input indices,
::tensorflow::Input shape
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `