pub fn eval_order<TI: TensorInfo>(model: &super::Model<TI>) -> TractResult<Vec<usize>> {
    let inputs = model.input_outlets()?.iter().map(|n| n.node).collect::<Vec<usize>>();
    let targets = model.output_outlets()?.iter().map(|n| n.node).collect::<Vec<usize>>();
    eval_order_for_nodes(model.nodes(), &inputs, &targets)
}
/// Find an evaluation order for a model, using its default inputs and outputs
/// as boundaries.
Find an evaluation order for a model, using its default inputs and outputs
as boundaries.
Find an evaluation order for a model using its default inputs and outputs as boundaries .
pub fn eval_order<TI: TensorInfo>(model: &super::Model<TI>) -> TractResult<Vec<usize>> {
    let inputs = model.input_outlets()?.iter().map(|n| n.node).collect::<Vec<usize>>();
    let targets = model.output_outlets()?.iter().map(|n| n.node).collect::<Vec<usize>>();
    eval_order_for_nodes(model.nodes(), &inputs, &targets)
}
pub fn eval_order < TI : TensorInfo > ( model : & super :: Model < TI > ) -> TractResult < Vec < usize > > { let inputs = model . input_outlets ( ) ? . iter ( ) . map ( | n | n . node ) . collect :: < Vec < usize > > ( ) ; let targets = model . output_outlets ( ) ? . iter ( ) . map ( | n | n . node ) . collect :: < Vec < usize > > ( ) ; eval_order_for_nodes ( model . nodes ( ) , & inputs , & targets ) }
Find an evaluation order for a model, using its default inputs and outputs
as boundaries.
Find an evaluation order for a model using its default inputs and outputs as boundaries .
model
&super::Model<TI>
model
&super::Model<TI>