.. _l-onnx-doc-SimplifiedLayerNormalization:

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SimplifiedLayerNormalization
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.. contents::
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.. _l-onnx-op-simplifiedlayernormalization-1:

SimplifiedLayerNormalization - 1
================================

**Version**

* **name**: `SimplifiedLayerNormalization (GitHub) <https://github.com/onnx/onnx/blob/main/docs/Operators.md#SimplifiedLayerNormalization>`_
* **domain**: **main**
* **since_version**: **1**
* **function**:
* **support_level**:
* **shape inference**:

This version of the operator has been available
**since version 1**.

**Summary**

SimplifiedLayerNormalization

**Attributes**

* **axis**:
  The first normalization dimension: normalization will be performed
  along dimensions axis : rank(inputs). Default value is ``?``.
* **epsilon**:
  The epsilon value to use to avoid division by zero. Default value is ``?``.
* **stash_type**:
  type used for stash mean/inv_std_var Default value is ``?``.

**Inputs**

* **X** (heterogeneous) - **T**:
  Input data tensor from the previous layer.
* **scale** (heterogeneous) - **V**:
  Scale tensor.

**Outputs**

Between 1 and 2 outputs.

* **Y** (heterogeneous) - **V**:
  Output data tensor.
* **inv_std_var** (optional, heterogeneous) - **U**:
  Saved inverse standard variance used during training to speed up
  gradient computation.

**Examples**