docs: def get_parameters(self):
          """
          Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.
  
          Returns
          -------
          parameters: list
              List of TabularCPDs, one for each variable of the model
  
          Examples
          --------
          >>> import numpy as np
          >>> import pandas as pd
          >>> from pgmpy.models import BayesianModel
          >>> from pgmpy.estimators import MaximumLikelihoodEstimator
          >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 4)),
          ...                       columns=['A', 'B', 'C', 'D'])
          >>> model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D'))
          >>> estimator = MaximumLikelihoodEstimator(model, values)
          >>> estimator.get_parameters()
          [<TabularCPD representing P(C:2) at 0x7f7b534251d0>,
          <TabularCPD representing P(B:2 | C:2, A:2) at 0x7f7b4dfd4da0>,
          <TabularCPD representing P(A:2) at 0x7f7b4dfd4fd0>,
          <TabularCPD representing P(D:2 | C:2) at 0x7f7b4df822b0>]
          """
          parameters = []
  
          for node in sorted(self.model.nodes()):
              cpd = self.estimate_cpd(node)
              parameters.append(cpd)
  
          return parameters