code: def confidence_interval():
      """Return the result of the confidence interval (ci) calculation."""
  
      def residual(pars, x, data=None):
          argu = (x * pars["decay"]) ** 2
          shift = pars["shift"]
          if abs(shift) > np.pi / 2:
              shift = shift - np.sign(shift) * np.pi
          model = (
              pars["amp"]
              * np.sin(shift + x / pars["period"])
              * np.exp(-argu)
          )
          if data is None:
              return model
          return model - data
  
      p_true = Parameters()
      p_true.add_many(
          ("amp", 14.0),
          ("period", 5.33),
          ("shift", 0.123),
          ("decay", 0.010),
      )
  
      x = np.linspace(0.0, 250.0, 2500)
      data = residual(p_true, x) + np.random.normal(
          scale=0.7215, size=x.size
      )
  
      fit_params = Parameters()
      fit_params.add_many(
          ("amp", 13.0), ("period", 2), ("shift", 0.0), ("decay", 0.02)
      )
  
      mini = Minimizer(
          residual, fit_params, fcn_args=(x,), fcn_kws={"data": data}
      )
      out = mini.leastsq()
      ci = conf_interval(mini, out)
      return ci
  
imports: import numpy
  import pytest
  import lmfit
  from lmfit import Minimizer, Parameters, ci_report, conf_interval, fit_report, report_ci, report_fit
  from lmfit.lineshapes import gaussian
  from lmfit.models import GaussianModel
  from lmfit.printfuncs import alphanumeric_sort, correl_table, fitreport_html_table, getfloat_attr, gformat
label_desc: Use image node_id 3 for calling a global function with example usage: confidence_interval() and returns: ci, unknown, model