new_contents: import numpy as np
  from numpy.testing import assert_equal
  
  from sklearn import linear_model
  from sklearn.utils import ransac
  
  
  def test_ransac_inliers_outliers():
      np.random.seed(1)
  
      # Generate coordinates of line
      X = np.arange(-200, 200)
      y = 0.2 * X + 20
      data = np.column_stack([X, y])
  
      # Add some faulty data
      outliers = np.array((10, 30, 200))
      data[outliers[0], :] = (1000, 1000)
      data[outliers[1], :] = (-1000, -1000)
      data[outliers[2], :] = (-100, -50)
  
      X = data[:, 0][:, np.newaxis]
      y = data[:, 1]
  
      # Estimate parameters of corrupted data
      inlier_mask = ransac(X, y, linear_model.LinearRegression(), 3, 5)
  
      # Ground truth / reference inlier mask
      ref_inlier_mask = np.ones_like(inlier_mask, dtype=np.bool_)
      ref_inlier_mask[outliers] = False
  
      assert_equal(inlier_mask, ref_inlier_mask)
  
  
  if __name__ == "__main__":
      np.testing.run_module_suite()
  
subject: Add simple RANSAC unit test