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

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

# instanceNorm

[common]\
fun [instanceNorm](instanceNorm.md)(input: [Tensor](../Tensor/index.md), 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)

`instanceNorm(input: Tensor, weight: Tensor? = null, bias: Tensor? = null, runningMean: Tensor? = null, runningVar: Tensor? = null, eps: Double? = null, activation: Activation? = null)`: the `instance_norm` operator. Instance normalization: statistics per `(sample, channel)` over the spatial dims (channels-last). With `running_mean` / `running_var` present they are folded instead of computing per-instance statistics (inference form; no training mode). Optional fused `activation` applies to the result.