code: def test_ransac_shape():
      # generate original data without noise
      model0 = CircleModel()
      model0.params = (10, 12, 3)
      t = np.linspace(0, 2 * np.pi, 1000)
      data0 = model0.predict_xy(t)
  
      # add some faulty data
      outliers = (10, 30, 200)
      data0[outliers[0], :] = (1000, 1000)
      data0[outliers[1], :] = (-50, 50)
      data0[outliers[2], :] = (-100, -10)
  
      # estimate parameters of corrupted data
      model_est, inliers = ransac(data0, CircleModel, 3, 5, rng=1)
      ransac(data0, CircleModel, 3, 5, rng=1)
  
      # test whether estimated parameters equal original parameters
      assert_almost_equal(model0.params, model_est.params)
      for outlier in outliers:
          assert outlier not in inliers
  
imports: import numpy
  import pytest
  from skimage._shared import testing
  from skimage._shared._warnings import expected_warnings
  from skimage._shared.testing import arch32, assert_almost_equal, assert_array_less, assert_equal, xfail
  from skimage.measure import CircleModel, EllipseModel, LineModelND, ransac
  from skimage.measure.fit import _dynamic_max_trials
  from skimage.transform import AffineTransform
label_desc: Use image node_id 21 for calling a global function with example usage: test_ransac_shape() without return types