code: def test_variables_are_sorted_alphabetically_in_custom_predict_method():
      causal_model = create_causal_model_from_equations(
          """
      A = norm(loc=0,scale=0.1)
      B = norm(loc=0, scale=0.1)
      Y = 0.5*B + 2*A+ norm(loc=0, scale=0.1)
      """
      )
      A = np.random.normal(0, 0.1, 10)
      B = np.random.normal(0, 0.1, 10)
      Y = 0.5 * B + 2 * A
  
      observations = pd.DataFrame({"A": A, "B": B, "Y": Y})
      eq_results = causal_model.causal_mechanism(
          "Y"
      )._prediction_model.predict(observations[["A", "B"]].to_numpy())
      assert np.array_equal(
          np.around(Y, 2), np.around(eq_results.ravel(), 2)
      )
  
file_path: dowhy/tests/gcm/test_equation_parser.py
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, create_causal_model_from_equations, fit
  from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel
  from dowhy.gcm.ml import create_linear_regressor_with_given_parameters
label_desc: Use image node_id 2 for calling a global function with example usage: test_variables_are_sorted_alphabetically_in_custom_predict_method() without return types