# GatherND {#openvino_docs_ops_movement_GatherND_5}

**Versioned name**: *GatherND-5*

**Category**: *Data movement*

**Short description**: *GatherND* gathers slices from input tensor into a tensor of a shape specified by indices.

**Detailed description**: *GatherND* gathers slices from `data` by `indices` and forms a tensor of a shape specified by `indices`.

`indices` is `K`-dimensional integer tensor or `K-1`-dimensional tensor of tuples with indices by which the operation gathers elements or slices
from `data` tensor. A position `i_0, ..., i_{K-2}` in the `indices` tensor corresponds to a tuple with indices `indices[i_0, ..., i_{K-2}]`
of a length equal to `indices.shape[-1]`. By this tuple with indices the operation gathers a slice or an element from `data` tensor and
insert it into the output at position `i_0, ..., i_{K-2}` as the following formula:

output[i_0, ..., i_{K-2},:,...,:] = data[indices[i_0, ..., i_{K-2}],:,...,:]

The last dimension of `indices` tensor must be not greater than a rank of `data` tensor, i.e. `indices.shape[-1] <= data.rank`.
The shape of the output can be computed as `indices.shape[:-1] + data.shape[indices.shape[-1]:]`.

Example 1 shows how *GatherND* operates with elements from `data` tensor:

```
indices = [[0, 0],
           [1, 0]]
data    = [[1, 2],
           [3, 4]]
output  = [1, 3]
```

Example 2 shows how *GatherND* operates with slices from `data` tensor:

```
indices = [[1], [0]]
data    = [[1, 2],
           [3, 4]]
output  = [[3, 4],
           [1, 2]]
```

Example 3 shows how *GatherND* operates when `indices` tensor has leading dimensions:

```
indices = [[[1]], [[0]]]
data    = [[1, 2],
           [3, 4]]
output  = [[[3, 4]],
           [[1, 2]]]
```

**Attributes**:

* *batch_dims*

  * **Description**: *batch_dims* (denoted as `b`) is a leading number of dimensions of `data` tensor 
    and `indices` representing the batches, and *GatherND* starts to gather from the `b+1` dimension.
    It requires the first `b` dimensions in `data` and `indices` tensors to be equal.
    In case of non-default value for *batch_dims*, the output shape is calculated as
`(multiplication of indices.shape[:b]) + indices.shape[b:-1] + data.shape[(indices.shape[-1] + b):]`.
    
    **NOTE:** The calculation of output shape is incorrect for non-default *batch_dims* value greater than one.
    For correct calculations use [GatherND_8](GatherND_8.md) operation**

  * **Range of values**: integer number and belongs to `[0; min(data.rank, indices.rank))`
  * **Type**: int
  * **Default value**: 0
  * **Required**: *no*

Example 4 shows how *GatherND* operates gathering elements for non-default *batch_dims* value:

```
batch_dims = 1
indices = [[1],    <--- this is applied to the first batch
           [0]]    <--- this is applied to the second batch, shape = (2, 1)
data    = [[1, 2], <--- the first batch
           [3, 4]] <--- the second batch, shape = (2, 2)
output  = [2, 3], shape = (2)
```

Example 5 shows how *GatherND* operates gathering slices for non-default *batch_dims* value:

```
batch_dims = 1
indices = [[1], <--- this is applied to the first batch
           [0]] <--- this is applied to the second batch, shape = (2, 1)
data    = [[[1,   2,  3,  4], [ 5,  6,  7,  8], [ 9, 10, 11, 12]]  <--- the first batch
           [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]] <--- the second batch, shape = (2, 3, 4)
output  = [[ 5,  6,  7,  8], [13, 14, 15, 16]], shape = (2, 4)
```

More complex example 6 shows how *GatherND* operates gathering slices with leading dimensions for non-default *batch_dims* value:

```
batch_dims = 2
indices = [[[[1]], <--- this is applied to the first batch
            [[0]],
            [[2]]],
           [[[0]],
            [[2]],
            [[2]]] <--- this is applied to the sixth batch
          ], shape = (2, 3, 1, 1)
data    = [[[1,   2,  3,  4], <--- this is the first batch
            [ 5,  6,  7,  8],
            [ 9, 10, 11, 12]]
           [[13, 14, 15, 16],
            [17, 18, 19, 20],
            [21, 22, 23, 24]] <--- this is the sixth batch
          ] <--- the second batch, shape = (2, 3, 4)
output  = [[2], [5], [11], [13], [19], [23]], shape = (6, 1)
```

**Inputs**:

* **1**:  `data` tensor of type *T*. This is a tensor of a rank not less than 1. **Required.**

* **2**:  `indices` tensor of type *T_IND*. This is a tensor of a rank not less than 1.
It requires that all indices from this tensor will be in a range `[0, s-1]` where `s` is corresponding dimension to which this index is applied.
Required.

**Outputs**:

*   **1**: Tensor with gathered values of type *T*.

**Types**

* *T*: any supported type.

* *T_IND*: any supported integer types.

**Examples**

```xml
<layer id="1" type="GatherND">
    <data batch_dims=0 />
    <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>
    </input>
    <output>
        <port id="3">
            <dim>25</dim>
            <dim>125</dim>
            <dim>15</dim>
        </port>
    </output>
</layer>
```

```xml
<layer id="1" type="GatherND">
    <data batch_dims=2 />
    <input>
        <port id="0">
            <dim>30</dim>
            <dim>2</dim>
            <dim>100</dim>
            <dim>35</dim>
        </port>
        <port id="1">
            <dim>30</dim>
            <dim>2</dim>
            <dim>3</dim>
            <dim>1</dim>
        </port>
    </input>
    <output>
        <port id="3">
            <dim>60</dim>
            <dim>3</dim>
            <dim>35</dim>
        </port>
    </output>
</layer>
```