code: def test_circle_model_insufficient_data():
      model = CircleModel()
      warning_message = [
          "Input does not contain enough significant data points."
      ]
      with expected_warnings(warning_message):
          model.estimate(np.array([[1, 2], [3, 4]]))
  
      with expected_warnings(warning_message):
          model.estimate(np.array([[0, 0], [1, 1], [2, 2]]))
  
      warning_message = (
          "Standard deviation of data is too small to estimate "
          "circle with meaningful precision."
      )
      with pytest.warns(
          RuntimeWarning, match=warning_message
      ) as _warnings:
          assert not model.estimate(np.ones((6, 2)))
      # Check that stacklevel is correct
      assert len(_warnings) == 1
      assert _warnings[0].filename == __file__
  
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 11 for calling a global function with example usage: test_circle_model_insufficient_data() without return types