docs: def sample(self, initial_pos, num_samples, trajectory_length, stepsize=None, return_type='dataframe'):
          """
          Method to return samples using Hamiltonian Monte Carlo
  
          Parameters
          ----------
          initial_pos: A 1d array like object
              Vector representing values of parameter position, the starting
              state in markov chain.
  
          num_samples: int
              Number of samples to be generated
  
          trajectory_length: int or float
              Target trajectory length, stepsize * number of steps(L),
              where L is the number of steps taken per HMC iteration,
              and stepsize is step size for splitting time method.
  
          stepsize: float , defaults to None
              The stepsize for proposing new values of position and momentum in simulate_dynamics
              If None, then will be choosen suitably
  
          return_type: string (dataframe | recarray)
              Return type for samples, either of 'dataframe' or 'recarray'.
              Defaults to 'dataframe'
  
          Returns
          -------
          sampled: A pandas.DataFrame or a numpy.recarray object depending upon return_type argument
  
          Examples
          --------
          >>> from pgmpy.sampling import HamiltonianMC as HMC, GradLogPDFGaussian, ModifiedEuler
          >>> from pgmpy.factors.continuous import GaussianDistribution as JGD
          >>> import numpy as np
          >>> mean = np.array([1, -1])
          >>> covariance = np.array([[1, 0.2], [0.2, 1]])
          >>> model = JGD(['x', 'y'], mean, covariance)
          >>> sampler = HMC(model=model, grad_log_pdf=GradLogPDFGaussian, simulate_dynamics=ModifiedEuler)
          >>> samples = sampler.sample(np.array([1, 1]), num_samples = 5,
          ...                          trajectory_length=6, stepsize=0.25, return_type='dataframe')
          >>> samples
                         x              y
          0   1.000000e+00   1.000000e+00
          1   1.592133e+00   1.152911e+00
          2   1.608700e+00   1.315349e+00
          3   1.608700e+00   1.315349e+00
          4   6.843856e-01   6.237043e-01
          >>> mean = np.array([4, 1, -1])
          >>> covariance = np.array([[1, 0.7 , 0.8], [0.7, 1, 0.2], [0.8, 0.2, 1]])
          >>> model = JGD(['x', 'y', 'z'], mean, covariance)
          >>> sampler = HMC(model=model, grad_log_pdf=GLPG)
          >>> samples = sampler.sample(np.array([1, 1]), num_samples = 10000,
          ...                          trajectory_length=6, stepsize=0.25, return_type='dataframe')
          >>> np.cov(samples.values.T)
          array([[ 1.00795398,  0.71384233,  0.79802097],
                 [ 0.71384233,  1.00633524,  0.21313767],
                 [ 0.79802097,  0.21313767,  0.98519017]])
          """
  
          self.accepted_proposals = 1.0
          initial_pos = _check_1d_array_object(initial_pos, 'initial_pos')
          _check_length_equal(initial_pos, self.model.variables, 'initial_pos', 'model.variables')
  
          if stepsize is None:
              stepsize = self._find_reasonable_stepsize(initial_pos)
  
          types = [(var_name, 'float') for var_name in self.model.variables]
          samples = np.zeros(num_samples, dtype=types).view(np.recarray)
  
          # Assigning after converting into tuple because value was being changed after assignment
          # Reason for this is unknown
          samples[0] = tuple(initial_pos)
          position_m = initial_pos
  
          lsteps = int(max(1, round(trajectory_length / stepsize, 0)))
          for i in range(1, num_samples):
  
              # Genrating sample
              position_m, _ = self._sample(position_m, trajectory_length, stepsize, lsteps)
              samples[i] = tuple(position_m)
  
          self.acceptance_rate = self.accepted_proposals / num_samples
  
          return _return_samples(return_type, samples)