# tf.compat.v1.scatter_update

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/state_ops.py#L382-L433) |

Applies sparse updates to a variable reference.

    tf.compat.v1.scatter_update(
        ref, indices, updates, use_locking=True, name=None
    )

This operation computes

        # Scalar indices
        ref[indices, ...] = updates[...]

        # Vector indices (for each i)
        ref[indices[i], ...] = updates[i, ...]

        # High rank indices (for each i, ..., j)
        ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]

This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.

If values in `ref` is to be updated more than once, because there are
duplicate entries in `indices`, the order at which the updates happen
for each value is undefined.

Requires `updates.shape = indices.shape + ref.shape[1:]`.
![](https://www.tensorflow.org/images/ScatterUpdate.png)

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| ## Args ||
|---|---|
| `ref` | A `Variable`. |
| `indices` | A `Tensor`. Must be one of the following types: `int32`, `int64`. A tensor of indices into the first dimension of `ref`. |
| `updates` | A `Tensor`. Must have the same type as `ref`. A tensor of updated values to store in `ref`. |
| `use_locking` | An optional `bool`. Defaults to `True`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. ||

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