code: def test_ransac_score():
      X = np.arange(100)[:, None]
      y = np.zeros((100,))
      y[0] = 1
      y[1] = 100
  
      estimator = LinearRegression()
      ransac_estimator = RANSACRegressor(
          estimator,
          min_samples=2,
          residual_threshold=0.5,
          random_state=0,
      )
      ransac_estimator.fit(X, y)
  
      assert ransac_estimator.score(X[2:], y[2:]) == 1
      assert ransac_estimator.score(X[:2], y[:2]) < 1
  
imports: import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from sklearn.datasets import make_regression
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit, RANSACRegressor, Ridge
  from sklearn.linear_model._ransac import _dynamic_max_trials
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 7 for calling a global function with example usage: test_ransac_score() without return types