---
title: "groupNorm"
sidebar_label: "groupNorm"
description: "Kotlin binding reference: groupNorm."
---

<!-- Generated by tools/api_reference/generate_api_docs.py. Do not edit. -->

//[clika-runtime](../../../index.md)/[io.clika.runtime](../index.md)/[Ops](index.md)/[groupNorm](groupNorm.md)

# groupNorm

[common]\
fun [groupNorm](groupNorm.md)(input: [Tensor](../Tensor/index.md), numGroups: [Long](https://kotlinlang.org/api/core/kotlin-stdlib/kotlin/-long/index.html), weight: [Tensor](../Tensor/index.md)? = null, bias: [Tensor](../Tensor/index.md)? = null, runningMean: [Tensor](../Tensor/index.md)? = null, runningVar: [Tensor](../Tensor/index.md)? = null, eps: [Double](https://kotlinlang.org/api/core/kotlin-stdlib/kotlin/-double/index.html)? = null, activation: [Activation](../Activation/index.md)? = null): [Tensor](../Tensor/index.md)

`groupNorm(input: Tensor, numGroups: Long, weight: Tensor? = null, bias: Tensor? = null, runningMean: Tensor? = null, runningVar: Tensor? = null, eps: Double? = null, activation: Activation? = null)`: the `group_norm` operator. Group normalization: channels split into `num_groups` groups, normalized per group (channels-last). Statistics are computed per `(sample, group)` over the group's channels and the spatial dims; with `running_mean` / `running_var` present they are folded per channel instead (batch-norm style). Optional fused `activation` applies to the result.