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gradients = function(list[unknown] model,
                     list[unknown] hyperparams,
                     matrix[double] features,
                     matrix[double] labels)
          return (list[unknown] gradients) {
  gradients = model;
}

aggregation = function(list[unknown] model,
                       list[unknown] hyperparams,
                       list[unknown] gradients)
   return (list[unknown] modelResult) {
  modelResult = model;
}

model = list(matrix(0, 2, 3))
X = matrix(1, 2, 3)
Y = matrix(2, 2, 3)
X_val = matrix(3, 2, 3)
Y_val = matrix(4, 2, 3)
hps = list()

# Use paramserv function
supd = ".defaultNS::gradients";
sagg = ".defaultNS::aggregation";
model = paramserv(model=model, k=$workers, features=X, labels=Y, val_features=X_val, val_labels=Y_val,
  upd=supd, agg=sagg, mode="LOCAL", utype="SBP", freq="EPOCH", scheme=$scheme, epochs=1, hyperparams=hps, num_backup_workers=$backup_workers)

print(toString(model))