code: def test_fit_one_stage():
      """
      Test to check the results for the one_stage linear fit.
      """
      msg = "Bounds for this fit have been set from experiments .*"
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore", message=msg, category=UserWarning
          )
          model = IvimModel(gtab, two_stage=False)
  
      fit = model.fit(data_single)
      linear_fit_params = [
          9.88834140e02,
          1.19707191e-01,
          7.91176970e-03,
          9.30095210e-04,
      ]
  
      linear_fit_signal = [
          988.83414044,
          971.77122546,
          955.46786293,
          939.87125905,
          924.93258982,
          896.85182201,
          870.90346447,
          846.81187693,
          824.34108781,
          803.28900104,
          783.48245048,
          764.77297789,
          747.03322866,
          669.54798887,
          605.03328304,
          549.00852235,
          499.21077611,
          454.40299244,
          413.83192296,
          376.98072773,
          343.45531017,
      ]
  
      assert_array_almost_equal(fit.model_params, linear_fit_params)
      assert_array_almost_equal(fit.predict(gtab), linear_fit_signal)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_raises, assert_array_less, assert_, assert_equal
  from dipy.testing import assert_greater_equal
  import pytest
  from dipy.reconst.ivim import ivim_prediction, IvimModel
  from dipy.core.gradients import gradient_table, generate_bvecs
  from dipy.sims.voxel import multi_tensor
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 22 for calling a global function with example usage: test_fit_one_stage() without return types