class_docstr: Random number generator base class used by bound module functions.
  
  Used to instantiate instances of Random to get generators that don't
  share state.
  
  Class Random can also be subclassed if you want to use a different basic
  generator of your own devising: in that case, override the following
  methods:  random(), seed(), getstate(), and setstate().
  Optionally, implement a getrandbits() method so that randrange()
  can cover arbitrarily large ranges.
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docstr: Normal distribution.
  
  mu is the mean, and sigma is the standard deviation.
docstr_tok: ["Normal","distribution",".","mu","is","the","mean",",","and","sigma","is","the","standard","deviation","."]
code: def normalvariate(self, mu=0.0, sigma=1.0):
      """Normal distribution.
  
      mu is the mean, and sigma is the standard deviation.
  
      """
      # Uses Kinderman and Monahan method. Reference: Kinderman,
      # A.J. and Monahan, J.F., "Computer generation of random
      # variables using the ratio of uniform deviates", ACM Trans
      # Math Software, 3, (1977), pp257-260.
  
      random = self.random
      while True:
          u1 = random()
          u2 = 1.0 - random()
          z = NV_MAGICCONST * (u1 - 0.5) / u2
          zz = z * z / 4.0
          if zz <= -_log(u2):
              break
      return mu + z * sigma
  
imports: from warnings import warn
  from math import log, exp, pi, e, ceil
  from math import sqrt, acos, cos, sin
  from math import tau, floor, isfinite
  from os import urandom
  from _collections_abc import Set, Sequence
  from operator import index
  from itertools import accumulate, repeat
  from bisect import bisect
  import os
  import _random
label_desc: Use image node_id 20 for calling the Random obj's underlying member method code with example usage: obj.normalvariate(mu, sigma) and returns: unknown