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.. _op_ai_onnx_GatherND-11:

GatherND - version 11
=====================

This page documents version **11** of operator **GatherND**. See :doc:`GatherND` for the latest version (since version 13).

- **Domain**: ``ai.onnx``
- **Since version**: 11

Given ``data`` tensor of rank ``r`` >= 1, and ``indices`` tensor of rank ``q`` >= 1, this operator gathers
slices of ``data`` into an output tensor of rank ``q + r - indices_shape[-1] - 1``.

``indices`` is an q-dimensional integer tensor, best thought of as a ``(q-1)``-dimensional tensor of index-tuples into ``data``,
where each element defines a slice of ``data``

Some salient points about the inputs' rank and shape:

1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks ``r`` and ``q``

2) The ``indices_shape[-1]`` should have a value between 1 (inclusive) and rank ``r`` (inclusive)

3) All values in ``indices`` are expected to be within bounds [-s, s-1] along axis of size ``s`` (i.e.) ``-data_shape[i]   r`` => error condition

2) If ``indices_shape[-1] == r``, since the rank of ``indices`` is ``q``, ``indices`` can be thought of as a ``(q-1)``-dimensional tensor
   containing 1-D tensors of dimension ``r``. Let us think of each such ``r`` ranked tensor as ``indices_slice``.
   Each \*scalar value\* corresponding to ``data[indices_slice]`` is filled into the corresponding location of the ``(q-1)``-dimensional tensor
   to form the ``output`` tensor (Example 1 below)

3) If ``indices_shape[-1] < r``, since the rank of ``indices`` is ``q``, ``indices`` can be thought of as a ``(q-1)``-dimensional tensor
   containing 1-D tensors of dimension ``< r``. Let us think of each such tensors as ``indices_slice``.
   Each \*tensor slice\* corresponding to ``data[indices_slice , :]`` is filled into the corresponding location of the ``(q-1)``-dimensional tensor
   to form the ``output`` tensor (Examples 2, 3, and 4 below)

This operator is the inverse of ``ScatterND``.

``Example 1``

.. code-block:: text

    data    = [[0,1],[2,3]]   # data_shape = [2, 2]

    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]

    output  = [0,3]           # output_shape = [2]

``Example 2``

.. code-block:: text

    data    = [[0,1],[2,3]]  # data_shape = [2, 2]

    indices = [[1],[0]]      # indices_shape = [2, 1]

    output  = [[2,3],[0,1]]  # output_shape = [2, 2]

``Example 3``

.. code-block:: text

    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]

    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]

    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]

``Example 4``

.. code-block:: text

    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]

    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]

    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]


**Inputs**

- **data** (*T*): Tensor of rank r >= 1.
- **indices** (*tensor(int64)*): Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.

**Outputs**

- **output** (*T*): Tensor of rank q + r - indices_shape[-1] - 1.

**Type Constraints**

- **T**: Constrain input and output types to any tensor type.
  Allowed types: tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).