code: def gaussian(x, mean, var, normed=True):
      """
      returns probability density function (pdf) for x given a Gaussian with the
      specified mean and variance. All must be scalars.
  
      gaussian (1,2,3) is equivalent to scipy.stats.norm(2, math.sqrt(3)).pdf(1)
      It is quite a bit faster albeit much less flexible than the latter.
  
      Parameters
      ----------
  
      x : scalar or array-like
          The value(s) for which we compute the distribution
  
      mean : scalar
          Mean of the Gaussian
  
      var : scalar
          Variance of the Gaussian
  
      normed : bool, default True
          Normalize the output if the input is an array of values.
  
      Returns
      -------
  
      pdf : float
          probability distribution of x for the Gaussian (mean, var). E.g. 0.101 denotes
          10.1%.
  
      Examples
      --------
  
      >>> gaussian(8, 1, 2)
      1.3498566943461957e-06
  
      >>> gaussian([8, 7, 9], 1, 2)
      array([1.34985669e-06, 3.48132630e-05, 3.17455867e-08])
      """
  
      pdf = ((2 * math.pi * var) ** -0.5) * np.exp(
          (-0.5 * (np.asarray(x) - mean) ** 2.0) / var
      )
      if normed and len(np.shape(pdf)) > 0:
          pdf = pdf / sum(pdf)
  
      return pdf
  
imports: from __future__ import absolute_import, division, unicode_literals
  import math
  from math import cos, sin
  import random
  import warnings
  import numpy
  from numpy.linalg import inv
  import scipy.linalg
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.stats import norm, multivariate_normal
label_desc: Use image node_id 6 for calling a global function with example usage: gaussian(x, mean, var, normed) and returns: pdf