gh-40826: Add some more "long time" annotations
    
Speed up one example so that it doesn't need `# long time`, and add the
annotation to some others that can't easily be sped up.
    
URL: #40826
Reported by: Michael Orlitzky
Reviewer(s): Frédéric Chapoton

Author: Release Manager

src/sage/matrix/matrix_gf2e_dense.pyx

@@ -1023,7 +1023,7 @@ cdef class Matrix_gf2e_dense(matrix_dense.Matrix_dense):
 
         EXAMPLES::
 
-            sage: m = Matrix(GL(2^8, GF(2^8)).random_element())
+            sage: m = Matrix(GL(2^6, GF(2^6)).random_element())
             sage: m.is_invertible()
             True
         """

src/sage/stats/distributions/discrete_gaussian_lattice.py

@@ -254,7 +254,7 @@ def _normalisation_factor_zz(self, tau=None, prec=None):
 
             sage: Sigma = Matrix(ZZ, [[5, -2, 4], [-2, 10, -5], [4, -5, 5]])
             sage: D = DGL(ZZ^3, Sigma, [7, 2, 5])
-            sage: D._normalisation_factor_zz()
+            sage: D._normalisation_factor_zz()  # long time
             78.6804...
 
             sage: M = Matrix(ZZ, [[1, 3, 0], [-2, 5, 1]])
@@ -489,22 +489,23 @@ def __init__(self, B, sigma=1, c=0, r=None, precision=None, sigma_basis=False):
             sage: c = vector(ZZ, [7, 2, 5])
             sage: D = distributions.DiscreteGaussianDistributionLatticeSampler(ZZ^n, Sigma, c)
             sage: f = D.f
-            sage: nf = D._normalisation_factor_zz(); nf # This has not been properly implemented
+            sage: # This has not been properly implemented...
+            sage: nf = D._normalisation_factor_zz(); nf  # long time
             78.6804...
 
         We can compute the expected number of samples before sampling a vector::
 
             sage: v = vector(ZZ, n, (11, 4, 8))
             sage: v.set_immutable()
-            sage: 1 / (f(v) / nf)
+            sage: 1 / (f(v) / nf)  # long time
             2553.9461...
 
             sage: counter = defaultdict(Integer); m = 0
             sage: while v not in counter:
             ....:     add_samples(1000)
             sage: sum(counter.values())  # random
             3000
-            sage: while abs(m*f(v)*1.0/nf/counter[v] - 1.0) >= 0.1:                     # needs sage.symbolic
+            sage: while abs(m*f(v)*1.0/nf/counter[v] - 1.0) >= 0.1:  # long time, needs sage.symbolic
             ....:     add_samples(1000)
 
         If the covariance provided is not positive definite, an error is thrown::

src/sage/symbolic/integration/integral.py

@@ -684,12 +684,12 @@ def integrate(expression, v=None, a=None, b=None, algorithm=None, hold=False):
     The following definite integral is not found by maxima::
 
         sage: f(x) = (x^4 - 3*x^2 + 6) / (x^6 - 5*x^4 + 5*x^2 + 4)
-        sage: integrate(f(x), x, 1, 2, algorithm='maxima')
+        sage: integrate(f(x), x, 1, 2, algorithm='maxima')  # long time
         integrate((x^4 - 3*x^2 + 6)/(x^6 - 5*x^4 + 5*x^2 + 4), x, 1, 2)
 
     but is nevertheless computed::
 
-        sage: integrate(f(x), x, 1, 2)
+        sage: integrate(f(x), x, 1, 2)  # long time
         -1/2*pi + arctan(8) + arctan(5) + arctan(2) + arctan(1/2)
 
     Both fricas and sympy give the correct result::