# tf.raw_ops.TensorScatterAdd

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Adds sparse `updates` to an existing tensor according to `indices`.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.TensorScatterAdd`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/TensorScatterAdd)

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    tf.raw_ops.TensorScatterAdd(
        tensor, indices, updates, name=None
    )

This operation creates a new tensor by adding sparse `updates` to the passed
in `tensor`.
This operation is very similar to [`tf.compat.v1.scatter_nd_add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/scatter_nd_add), except that the
updates are added onto an existing tensor (as opposed to a variable). If the
memory for the existing tensor cannot be re-used, a copy is made and updated.

`indices` is an integer tensor containing indices into a new tensor of shape
`tensor.shape`. The last dimension of `indices` can be at most the rank of
`tensor.shape`:

    indices.shape[-1] <= tensor.shape.rank

The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = tensor.shape.rank`) or slices
(if `indices.shape[-1] < tensor.shape.rank`) along dimension
`indices.shape[-1]` of `tensor.shape`. `updates` is a tensor with shape

    indices.shape[:-1] + tensor.shape[indices.shape[-1]:]

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

In Python, this scatter add operation would look like this:

    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    tensor = tf.ones([8], dtype=tf.int32)
    updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
    updated
    <tf.Tensor: shape=(8,), dtype=int32,
    numpy=array([ 1, 12,  1, 11, 10,  1,  1, 13], dtype=int32)>

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 add 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]]])
    tensor = tf.ones([4, 4, 4],dtype=tf.int32)
    updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
    updated
    <tf.Tensor: shape=(4, 4, 4), dtype=int32,
    numpy=array([[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
                 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
                 [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
                 [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int32)>

> [!NOTE]
> **Note:** on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

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| ## Args ||
|---|---|
| `tensor` | A `Tensor`. Tensor to copy/update. |
| `indices` | A `Tensor`. Must be one of the following types: `int32`, `int64`. Index tensor. |
| `updates` | A `Tensor`. Must have the same type as `tensor`. Updates to scatter into output. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `tensor`. ||

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