code: def _generate_data():
      X0 = np.random.uniform(-1, 1, 1000)
      X1 = 2 * X0 + np.random.normal(0, 0.1, 1000)
      X2 = 0.5 * X0 + np.random.normal(0, 0.1, 1000)
      X3 = 0.5 * X2 + np.random.normal(0, 0.1, 1000)
      original_observations = pd.DataFrame(
          {"X0": X0, "X1": X1, "X2": X2, "X3": X3}
      )
  
      X0 = np.random.uniform(-1, 1, 1000)
      X1 = 2 * X0 + np.random.normal(0, 0.1, 1000)
      X2 = 2 * X0 + np.random.normal(0, 0.1, 1000)
      X3 = 3 * X2 + np.random.normal(0, 0.1, 1000)
      outlier_observations = pd.DataFrame(
          {"X0": X0, "X1": X1, "X2": X2, "X3": X3}
      )
  
      return original_observations, outlier_observations
  
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from pytest import approx
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, distribution_change, distribution_change_of_graphs, fit
  from dowhy.gcm.auto import AssignmentQuality
  from dowhy.gcm.distribution_change import mechanism_change_test
  from dowhy.gcm.ml import create_linear_regressor
  from dowhy.gcm.shapley import ShapleyConfig
label_desc: Use image node_id 8 for calling a global function with example usage: _generate_data() and returns: original_observations,  outlier_observations