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+# scatterElements
+
+**Table Of Contents**
+- [Description](#description)
+    * [Structure](#structure)
+- [Parameters](#parameters)
+- [Additional resources](#additional-resources)
+- [License](#license)
+- [Changelog](#changelog)
+- [Known issues](#known-issues)
+
+## Description
+
+The scatterElements plugin implements the scatter operation described in (https://github.com/rusty1s/pytorch_scatter), in compliance with the [ONNX specification for ScatterElements](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ScatterElements)
+
+Note: ScatterElements with reduce="none" is implemented in TRT core, not this plugin.
+
+### Structure
+
+This plugin has the 2 versions. The latest is plugin creator class `ScatterElementsPluginV3Creator` and the plugin class `ScatterElementsPluginV3` which extends `IPluginV3`. (name: `ScatterElements`, version: 2)
+The legacy plugin that will be deprecated, is plugin creator class `ScatterElementsPluginV2Creator` and the plugin class `ScatterElementsPluginV2`, which extends `IPluginV2DynamicExt` (name: `ScatterElements`, version: 1).
+
+The `ScatterElements` plugin consumes the following inputs:
+
+1. `data` - T: Tensor of rank r >= 1.
+2. `indices` - Tind: Tensor of int64 indices, of r >= 1 (same rank as input). 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.
+3. `updates` - T: Tensor of rank r >=1 (same rank and shape as indices)
+
+The `ScatterElements` plugin produces the following output:
+
+1. `output` - T: Tensor, same shape as `data`.
+
+## Parameters
+
+The `ScatterElements` plugin has the following parameters:
+
+| Type             | Parameter                       | Description
+|------------------|---------------------------------|--------------------------------------------------------
+|`int`             |`axis`                           | Which axis to scatter on. Default is 0. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
+|`char`            |`reduction`                      | Type of reduction to apply: add, mul, max, min. ‘add’: reduction using the addition operation. ‘mul’: reduction using the multiplication operation.‘max’: reduction using the maximum operation.‘min’: reduction using the minimum operation.
+
+
+The following resources provide a deeper understanding of the `scatterElements` plugin:
+
+- [pytorch_scatter: original implementation and docs](https://github.com/rusty1s/pytorch_scatter)
+- [ONNX specification for ScatterElements](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ScatterElements)
+
+## License
+
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html)
+documentation.
+
+## Changelog
+
+- July 2024: Version 2 of the plugin migrated to `IPluginV3` interface design. The legacy plugin (version 1) using `IPluginV2DynamicExt` interface is deprecated.
+- Oct 2023: This is the first release of this `README.md` file.
+
+## Known issues
+
+- Types T=BFLOAT16 and T=INT8 are currently not supported.
+- ONNX spec allows Tind=int32 : only INT64 is supported by this plugin