class_docstr: In its most common representation, a multivariate Gaussian distribution
  over X1, X2, ..., Xn is characterized by an n-dimensional mean vector μ,
  and a symmetric n x n covariance matrix Σ.
  
  This is the base class for its representation.
docstr: Returns the probability density function(pdf).
  
  Returns
  -------
  function: The probability density function of the distribution.
  
  Examples
  --------
  >>> from pgmpy.factors.distributions import GaussianDistribution
  >>> dist = GD(variables=['x1', 'x2', 'x3'],
  ...           mean=[1, -3, 4],
  ...           cov=[[4, 2, -2],
  ...                [2, 5, -5],
  ...                [-2, -5, 8]])
  >>> dist.pdf
  <function pgmpy.factors.distributions.GaussianDistribution.GaussianDistribution.pdf.<locals>.<lambda>>
  >>> dist.pdf([0, 0, 0])
  0.0014805631279234139
docstr_tok: ["Returns","the","probability","density","function","(","pdf",")",".","Returns","--","--","--","-","function",":","The","probability","density","function","of","the","distribution",".","Examples","--","--","--","--",">",">",">","from","pgmpy.factors.distributions","import","GaussianDistribution",">",">",">","dist","=","GD","(","variables=","[","'x1","'",",","'x2","'",",","'x3","'","]",",","...","mean=","[","1",",","-3",",","4","]",",","...","cov=","[","[","4",",","2",",","-2","]",",","...","[","2",",","5",",","-5","]",",","...","[","-2",",","-5",",","8","]","]",")",">",">",">","dist.pdf","<","function","pgmpy.factors.distributions.GaussianDistribution.GaussianDistribution.pdf.","<","locals",">",".","<","lambda",">",">",">",">",">","dist.pdf","(","[","0",",","0",",","0","]",")","0.0014805631279234139"]
code: def pdf(self):
      """
      Returns the probability density function(pdf).
  
      Returns
      -------
      function: The probability density function of the distribution.
  
      Examples
      --------
      >>> from pgmpy.factors.distributions import GaussianDistribution
      >>> dist = GD(variables=['x1', 'x2', 'x3'],
      ...           mean=[1, -3, 4],
      ...           cov=[[4, 2, -2],
      ...                [2, 5, -5],
      ...                [-2, -5, 8]])
      >>> dist.pdf
      <function pgmpy.factors.distributions.GaussianDistribution.GaussianDistribution.pdf.<locals>.<lambda>>
      >>> dist.pdf([0, 0, 0])
      0.0014805631279234139
      """
      return lambda *args: multivariate_normal.pdf(
          args,
          self.mean.reshape(1, len(self.variables))[0],
          self.covariance,
      )
  
imports: import numpy
  from scipy.stats import multivariate_normal
  from pgmpy.factors.distributions import BaseDistribution
label_desc: Use image node_id 2 for calling the GaussianDistribution obj's underlying member method code with example usage: obj.pdf() and returns: function