attention_width         Parameters for the tabnet model
autoplot.tabnet_explain
                        Plot tabnet_explain mask importance heatmap
autoplot.tabnet_fit     Plot tabnet_fit model loss along epochs
build_ancestor_matrix_from_outcomes
                        Build ancestor matrix aligned with observed
                        outcome classes
cat_emb_dim             Non-tunable parameters for the tabnet model
check_compliant_node    Check that Node object names are compliant
entmax                  Alpha-entmax
get_constr_output       Apply hierarchy constraints via max-pooling
                        over descendants (MCM)
get_tau                 Optimal threshold (tau) computation for
                        1.5-entmax
nn_aum_loss             AUM loss
nn_mc_loss              Max-Constraint Margin Loss (module)
nn_prune_head.tabnet_fit
                        Prune top layer(s) of a tabnet network
nnf_mc_loss             Max-Constraint Margin Loss (functional)
nnf_multilabel_one_hot
                        Convert class_id tensor to binary one-hot
                        tensor
node_to_df              Turn a Node object into predictor and outcome.
predict.tabnet_fit      Predict using 'tabnet'
sparsemax               Sparsemax
tabnet                  Parsnip compatible tabnet model
tabnet_config           Configuration for TabNet models
tabnet_explain          Interpretation metrics from a TabNet model
tabnet_fit              Tabnet model
tabnet_nn               TabNet Model Architecture
tabnet_pretrain         Tabnet model
