ai.onnx.Mean

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Computes the elementwise mean of one or more input tensors with multidirectional (NumPy-style) broadcasting. All inputs and the output share the same data type.

See the ONNX Mean spec for the reference semantics.

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
A a T First input tensor. required
B b T Second input tensor, broadcast-compatible with A. optional
C c T Third input tensor, broadcast-compatible with A and B. optional
D d T Fourth input tensor, broadcast-compatible with A, B, and C. optional
E e T Fifth input tensor, broadcast-compatible with all other inputs. optional

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
mean y T derived derived; see description Elementwise mean of all provided input tensors. required

Type constraints

Variable Allowed dtypes
T float32, float16

Files

Use with @huggingface/kernels

The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.

Replace each *Data placeholder with a typed array containing the corresponding input data.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.Mean", { version: 1 });
const { y } = await kernel({ a: { data: aData, shape: [] } });
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Requires WebGPU support. See the compatibility table.