class_docstr: Class for finding gradient and gradient log of Joint Gaussian Distribution
  Inherits pgmpy.inference.base_continuous.BaseGradLogPDF
  Here model must be an instance of GaussianDistribution
  
  Parameters
  ----------
  variable_assignments : A 1d array like object (numpy.ndarray or list)
      Vector representing values of variables at which we want to find gradient and log
  
  model : An instance of pgmpy.models.GaussianDistribution
  
  Example
  -------
  >>> from pgmpy.sampling import GradLogPDFGaussian
  >>> from pgmpy.factors.continuous import GaussianDistribution
  >>> import numpy as np
  >>> mean = np.array([3, 4])
  >>> covariance = np.array([[5, 4], [4, 5]])
  >>> model = GaussianDistribution(['x', 'y'], mean, covariance)
  >>> dist_param = np.array([12, 21])
  >>> grad_logp, logp = GradLogPDFGaussian(dist_param, model).get_gradient_log_pdf()
  >>> logp
  -34.777777777777771
  >>> grad_logp
  array([ 2.55555556, -5.44444444])
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code: def __init__(self, variable_assignments, model):
      BaseGradLogPDF.__init__(self, variable_assignments, model)
      self.grad_log, self.log_pdf = self._get_gradient_log_pdf()
  
imports: import itertools
  import math
  import os
  import networkx
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from pgmpy.inference import Inference
  from pgmpy.utils import _check_1d_array_object, _check_length_equal, compat_fns
label_desc: Use image node_id 1 to create a new GradLogPDFGaussian object from inherited base classes: BaseGradLogPDF with example: obj = GradLogPDFGaussian(variable_assignments, model)