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: Exponential distribution.
  
  lambd is 1.0 divided by the desired mean.  It should be
  nonzero.  (The parameter would be called "lambda", but that is
  a reserved word in Python.)  Returned values range from 0 to
  positive infinity if lambd is positive, and from negative
  infinity to 0 if lambd is negative.
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code: def expovariate(self, lambd):
      """Exponential distribution.
  
      lambd is 1.0 divided by the desired mean.  It should be
      nonzero.  (The parameter would be called "lambda", but that is
      a reserved word in Python.)  Returned values range from 0 to
      positive infinity if lambd is positive, and from negative
      infinity to 0 if lambd is negative.
  
      """
      # lambd: rate lambd = 1/mean
      # ('lambda' is a Python reserved word)
  
      # we use 1-random() instead of random() to preclude the
      # possibility of taking the log of zero.
      return -_log(1.0 - self.random()) / lambd
  
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 23 for calling the Random obj's underlying member method code with example usage: obj.expovariate(lambd) and returns: unknown