# ScatterNDUpdate {#openvino_docs_ops_movement_ScatterNDUpdate_3}

**Versioned name**: *ScatterNDUpdate-3*

**Category**: *Data movement*

**Short description**: Creates a copy of the first input tensor with updated elements specified with second and third input tensors.

**Detailed description**: The operation produces a copy of `data` tensor and updates its value to values specified
by `updates` at specific index positions specified by `indices`. The output shape is the same as the shape of `data`.
`indices` tensor must not have duplicate entries. In case of duplicate entries in `indices` the result is undefined.

The last dimension of `indices` can be at most the rank of `data.shape`.
The last dimension of `indices` corresponds to indices into elements if `indices.shape[-1]` = `data.shape.rank` or slices
if `indices.shape[-1]` < `data.shape.rank`. `updates` is a tensor with shape `indices.shape[:-1] + data.shape[indices.shape[-1]:]`

Example 1 that shows update of four single elements in `data`:

```
data    = [1, 2, 3, 4, 5, 6, 7, 8]
indices = [[4], [3], [1], [7]]
updates = [9, 10, 11, 12]
output  = [1, 11, 3, 10, 9, 6, 7, 12]
```

Example 2 that shows update of two slices of `4x4` shape in `data`:

```
data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
           [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
           [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
           [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
indices = [[0], [2]]
updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
           [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
           [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
           [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
           [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
```


**Attributes**: *ScatterNDUpdate* does not have attributes.

**Inputs**:

*   **1**: `data` tensor of arbitrary rank `r` >= 1 and of type *T*. **Required.**

*   **2**: `indices` tensor with indices of arbitrary rank `q` >= 1 and of type *T_IND*. All index values `i_j` in index entry `(i_0, i_1, ...,i_k)` (where `k = indices.shape[-1]`) must be within bounds `[0, s_j - 1]` where `s_j = data.shape[j]`. `k` must be at most `r`. **Required.**

*   **3**: `updates` tensor of rank `r - indices.shape[-1] + q - 1` of type *T*. If expected `updates` rank is 0D it can be a tensor with single element. **Required.**

**Outputs**:

*   **1**: tensor with shape equal to `data` tensor of the type *T*.

**Types**

* *T*: any numeric type.

* *T_IND*: `int32` or `int64`

**Example**

```xml
<layer ... type="ScatterNDUpdate">
    <input>
        <port id="0">
            <dim>1000</dim>
            <dim>256</dim>
            <dim>10</dim>
            <dim>15</dim>
        </port>
        <port id="1">
            <dim>25</dim>
            <dim>125</dim>
            <dim>3</dim>
        </port>
        <port id="2">
            <dim>25</dim>
            <dim>125</dim>
            <dim>15</dim>
        </port>
    </input>
    <output>
        <port id="3">
            <dim>1000</dim>
            <dim>256</dim>
            <dim>10</dim>
            <dim>15</dim>
        </port>
    </output>
</layer>
```