use crate::{arc, define_obj_type, ml, ns, objc};
define_obj_type!(
#[doc(alias = "MLModelDescription")]
pub ModelDesc(ns::Id)
);
impl ModelDesc {
#[objc::msg_send(inputDescriptionsByName)]
pub fn input_descs_by_name(&self) -> arc::R<ns::Dictionary<ns::String, ml::FeatureDesc>>;
#[objc::msg_send(outputDescriptionsByName)]
pub fn output_descs_by_name(&self) -> arc::R<ns::Dictionary<ns::String, ml::FeatureDesc>>;
#[objc::msg_send(stateDescriptionsByName)]
pub fn state_descs_by_name(&self) -> arc::R<ns::Dictionary<ns::String, ml::FeatureDesc>>;
#[objc::msg_send(predictedFeatureName)]
pub fn predicted_feature_name(&self) -> Option<arc::R<ns::String>>;
#[objc::msg_send(predictedProbabilitiesName)]
pub fn predicted_probabilities_name(&self) -> Option<arc::R<ns::String>>;
#[objc::msg_send(metadata)]
pub fn metadata(&self) -> ns::Dictionary<ns::String, ns::Id>;
#[objc::msg_send(classLabels)]
pub fn class_labels(&self) -> Option<arc::R<ns::Array<ns::Id>>>;
}
impl ModelDesc {
#[objc::msg_send(isUpdatable)]
pub fn is_updatable(&self) -> bool;
#[objc::msg_send(trainingInputDescriptionsByName)]
pub fn training_input_descs_by_name(
&self,
) -> arc::R<ns::Dictionary<ns::String, ml::FeatureDesc>>;
}