def METHOD_NAME(self):
    if not any((s.is_linked for s in self.outputs)):
        return
    need_eval = self.outputs['Vertices'].is_linked
    curves_s = self.inputs['Curve'].sv_get()
    resolution_s = self.inputs['Resolution'].sv_get()
    length_s = self.inputs['Length'].sv_get()
    samples_s = self.inputs['Samples'].sv_get(default=[[]])
    length_s = ensure_nesting_level(length_s, 3)
    resolution_s = ensure_nesting_level(resolution_s, 2)
    samples_s = ensure_nesting_level(samples_s, 2)
    curves_s = ensure_nesting_level(curves_s, 2, data_types=(SvCurve,))
    ts_out = []
    verts_out = []
    for curves, resolutions, input_lengths_i, samples_i in zip_long_repeat(curves_s, resolution_s, length_s, samples_s):
        for curve, resolution, input_lengths, samples in zip_long_repeat(curves, resolutions, input_lengths_i, samples_i):
            mode = self.mode
            accuracy = self.accuracy
            if self.id_data.sv_draft:
                mode = 'LIN'
                accuracy = self.accuracy_draft
            if self.specify_accuracy:
                tolerance = 10 ** (-accuracy)
            else:
                tolerance = None
            solver = SvCurveLengthSolver(curve)
            solver.prepare(mode, resolution, tolerance=tolerance)
            if self.eval_mode == 'AUTO':
                total_length = solver.get_total_length()
                input_lengths = np.linspace(0.0, total_length, num = samples)
            else:
                input_lengths = np.array(input_lengths)
            ts = solver.solve(input_lengths)
            ts_out.append(ts.tolist())
            if need_eval:
                verts = curve.evaluate_array(ts).tolist()
                verts_out.append(verts)
    self.outputs['T'].sv_set(ts_out)
    self.outputs['Vertices'].sv_set(verts_out)