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: Normalizes the distribution. In case of a Gaussian Distribution the
  distribution is always normalized, therefore this method doesn't do
  anything and has been implemented only for a consistent API across
  distributions.
docstr_tok: ["Normalizes","the","distribution",".","In","case","of","a","Gaussian","Distribution","the","distribution","is","always","normalized",",","therefore","this","method","does","n't","do","anything","and","has","been","implemented","only","for","a","consistent","API","across","distributions","."]
code: def normalize(self, inplace=True):
      """
      Normalizes the distribution. In case of a Gaussian Distribution the
      distribution is always normalized, therefore this method doesn't do
      anything and has been implemented only for a consistent API across
      distributions.
      """
      phi = self if inplace else self.copy()
  
      # The pdf of a Joint Gaussian distribution is always
      # normalized. Hence, no changes.
      if not inplace:
          return phi
  
imports: import numpy
  from scipy.stats import multivariate_normal
  from pgmpy.factors.distributions import BaseDistribution
label_desc: Use image node_id 7 for calling the GaussianDistribution obj's underlying member method code with example usage: obj.normalize(inplace) and returns: phi