code: def reduce_fit(interface, state, label, inp):
      
      import numpy as np
  
      out = interface.output(0)
      sum_etde = 0
      sum_ete = [0 for _ in range(len(state["X_indices"]) + 1)]
      for key, value in inp:
          if key == "etde":
              sum_etde += value
          else:
              sum_ete[key] += value
  
      sum_ete += np.true_divide(np.eye(len(sum_ete)), state["nu"])
      out.add("params", np.linalg.lstsq(sum_ete, sum_etde)[0])
docs: Function joins all partially calculated matrices ETE and ETDe, aggregates them and it calculates final parameters.