code: def evaluate(self, inputs):
          
          y = super(Empirical1D, self).evaluate(inputs)
  
          # Assume NaN at both ends need to be extrapolated based on
          # nearest end point.
          if self.fill_value is np.nan:
              # Cannot use sampleset() due to ExtinctionModel1D
              x = np.squeeze(self.points)
  
              if np.isscalar(y):  # pragma: no cover
                  if inputs < x[0]:
                      y = self.lookup_table[0]
                  elif inputs > x[-1]:
                      y = self.lookup_table[-1]
              else:
                  y[inputs < x[0]] = self.lookup_table[0]
                  y[inputs > x[-1]] = self.lookup_table[-1]
  
          return self._process_neg_flux(inputs, y)
docs: Evaluate the model.
  
          Parameters
          ----------
          inputs : number or ndarray
              Wavelengths in same unit as ``points``.
  
          Returns
          -------
          y : number or ndarray
              Flux or throughput in same unit as ``lookup_table``.