code: def test_maximum_likelihood_estimator(self):
      # Obtain the X and Y which are jointly gaussian from the distribution
      # beta = [2, 0.7, 0.3]
      sigma_c = 4
  
      x_df = pd.read_csv(
          "pgmpy/tests/test_factors/test_continuous/gbn_values_1.csv"
      )
  
      mu = np.array([7, 13])
      sigma = np.array([[4, 3], [3, 6]])
  
      cpd1 = LinearGaussianCPD(
          "Y",
          evidence_mean=mu,
          evidence_variance=sigma,
          evidence=["X1", "X2"],
      )
      mean, variance = cpd1.fit(
          x_df, states=["(Y|X)", "X1", "X2"], estimator="MLE"
      )
      np_test.assert_allclose(
          mean, [2.361152, 0.693147, 0.276383], rtol=1e-03
      )
      np_test.assert_allclose(variance, sigma_c, rtol=1e-1)
  
file_path: pgmpy/pgmpy/tests/test_factors/test_continuous/test_Linear_Gaussian_CPD.py
imports: import unittest
  import numpy.testing
  import pandas
  import numpy
  from pgmpy.factors.continuous import LinearGaussianCPD
label_desc: Use image node_id 2 for calling the TestLGCPD obj's underlying member method code with example usage: obj.test_maximum_likelihood_estimator() without return types