Whitespace normalization and backport:

random.gauss() uses a piece of hidden state used by nothing else,
and the .seed() and .whseed() methods failed to reset it.  In other
words, setting the seed didn't completely determine the sequence of
results produced by random.gauss().  It does now.  Programs repeatedly
mixing calls to a seed method with calls to gauss() may see different
results now.

Bugfix candidate (random.gauss() has always been broken in this way),
despite that it may change results.

Lib/random.py

@@ -116,7 +116,6 @@ class Random:
         """
 
         self.seed(x)
-        self.gauss_next = None
 
 ## -------------------- core generator -------------------
 
@@ -150,6 +149,8 @@ class Random:
         a, z = divmod(a, 30322)
         self._seed = int(x)+1, int(y)+1, int(z)+1
 
+        self.gauss_next = None
+
     def random(self):
         """Get the next random number in the range [0.0, 1.0)."""
 
@@ -238,6 +239,8 @@ class Random:
         # Zero is a poor seed, so substitute 1
         self._seed = (x or 1, y or 1, z or 1)
 
+        self.gauss_next = None
+
     def whseed(self, a=None):
         """Seed from hashable object's hash code.
 
@@ -444,7 +447,7 @@ class Random:
     def gammavariate(self, alpha, beta):
 
         # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
-        
+
         # Warning: a few older sources define the gamma distribution in terms
         # of alpha > -1.0
         if alpha <= 0.0 or beta <= 0.0:
@@ -460,7 +463,7 @@ class Random:
             ainv = _sqrt(2.0 * alpha - 1.0)
             bbb = alpha - LOG4
             ccc = alpha + ainv
-            
+
             while 1:
                 u1 = random()
                 u2 = random()
@@ -627,7 +630,7 @@ def _test(N=20000):
     _test_generator(N, 'vonmisesvariate(0.0, 1.0)')
     _test_generator(N, 'gammavariate(0.01, 1.0)')
     _test_generator(N, 'gammavariate(0.1, 1.0)')
-    _test_generator(N, 'gammavariate(0.1, 2.0)')    
+    _test_generator(N, 'gammavariate(0.1, 2.0)')
     _test_generator(N, 'gammavariate(0.5, 1.0)')
     _test_generator(N, 'gammavariate(0.9, 1.0)')
     _test_generator(N, 'gammavariate(1.0, 1.0)')