code: def test_pearsonr(self):
      coef, p_value = pearsonr(
          X="X", Y="Y", Z=[], data=self.df_ind, boolean=False
      )
      self.assertTrue(coef < 0.1)
      self.assertTrue(p_value > 0.05)
  
      coef, p_value = pearsonr(
          X="X", Y="Y", Z=["Z"], data=self.df_cind, boolean=False
      )
      self.assertTrue(coef < 0.1)
      self.assertTrue(p_value > 0.05)
  
      coef, p_value = pearsonr(
          X="X",
          Y="Y",
          Z=["Z1", "Z2"],
          data=self.df_cind_mul,
          boolean=False,
      )
      self.assertTrue(coef < 0.1)
      self.assertTrue(p_value > 0.05)
  
      coef, p_value = pearsonr(
          X="X", Y="Y", Z=["Z"], data=self.df_vstruct, boolean=False
      )
      self.assertTrue(abs(coef) > 0.9)
      self.assertTrue(p_value < 0.05)
  
      # Tests for when boolean=True
      self.assertTrue(
          pearsonr(
              X="X",
              Y="Y",
              Z=[],
              data=self.df_ind,
              significance_level=0.05,
          )
      )
      self.assertTrue(
          pearsonr(
              X="X",
              Y="Y",
              Z=["Z"],
              data=self.df_cind,
              significance_level=0.05,
          )
      )
      self.assertTrue(
          pearsonr(
              X="X",
              Y="Y",
              Z=["Z1", "Z2"],
              data=self.df_cind_mul,
              significance_level=0.05,
          )
      )
      self.assertFalse(
          pearsonr(
              X="X",
              Y="Y",
              Z=["Z"],
              data=self.df_vstruct,
              significance_level=0.05,
          )
      )
  
file_path: pgmpy/pgmpy/tests/test_estimators/test_CITests.py
imports: import unittest
  import math
  import numpy
  import pandas
  from numpy import testing
  from pgmpy.estimators.CITests import *
label_desc: Use image node_id 2 for calling the TestPearsonr obj's underlying member method code with example usage: obj.test_pearsonr() without return types