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//! # Mean
//!
//! Element-wise mean of each of the input tensors with multidirectional (Numpy-style)
//! broadcasting support. All inputs and outputs must have the same data type.
//!
//! **ONNX Spec**: <https://onnx.ai/onnx/operators/onnx__Mean.html>
//!
//! ## Opset Versions
//!
//! - **Opset 1-5**: Basic element-wise mean
//! - **Opset 6-7**: Improved broadcasting support
//! - **Opset 8**: Multidirectional (Numpy-style) broadcasting
//! - **Opset 13**: Extended type support including bfloat16
use onnx_ir_derive::NodeBuilder;
use crate::ir::{Argument, Node, RawNode};
use crate::processor::{
InputSpec, NodeProcessor, NodeSpec, OutputPreferences, OutputSpec, ProcessError,
same_as_input_broadcast,
};
/// Node representation for Mean operation
#[derive(Debug, Clone, NodeBuilder)]
pub struct MeanNode {
pub name: String,
pub inputs: Vec<Argument>,
pub outputs: Vec<Argument>,
}
/// Node processor for Mean operation (variadic element-wise mean)
pub(crate) struct MeanProcessor;
impl NodeProcessor for MeanProcessor {
type Config = ();
fn spec(&self) -> NodeSpec {
NodeSpec {
min_opset: 1,
max_opset: None,
inputs: InputSpec::AtLeast(1),
outputs: OutputSpec::Exact(1),
}
}
fn infer_types(
&self,
node: &mut RawNode,
_opset: usize,
_output_preferences: &OutputPreferences,
) -> Result<(), ProcessError> {
same_as_input_broadcast(node);
Ok(())
}
fn build_node(&self, builder: RawNode, _opset: usize) -> Node {
Node::Mean(MeanNode {
name: builder.name,
inputs: builder.inputs,
outputs: builder.outputs,
})
}
}