Cosmetics

src/minimization/kl_energies.py

@@ -416,7 +416,7 @@ def MetricGaussianKL(mean, hamiltonian, n_samples, mirror_samples, constants=[],
 
 
 def GeoMetricKL(mean, hamiltonian, n_samples, minimizer_samp, mirror_samples,
-                start_from_lin = True, constants=[], point_estimates=[],
+                start_from_lin=True, constants=[], point_estimates=[],
                 napprox=0, comm=None, nanisinf=True):
     """Provides the sampled Kullback-Leibler used in geometric Variational
     Inference (geoVI).
@@ -487,10 +487,10 @@ def GeoMetricKL(mean, hamiltonian, n_samples, minimizer_samp, mirror_samples,
     As in MGVI, mirroring samples can help to stabilize the latent mean as it
     reduces sampling noise. But unlike MGVI a mirrored sample involves an
     additional solve of the non-linear transformation. Therefore, when using
-    MPI, the mirrored samples also get distributed if enough tasks are available.
-    If there are more total samples than tasks, the mirrored counterparts
-    try to reside on the same task as their non mirrored partners. This ensures
-    that at least the starting position can be re-used.
+    MPI, the mirrored samples also get distributed if enough tasks are
+    available.  If there are more total samples than tasks, the mirrored
+    counterparts try to reside on the same task as their non mirrored partners.
+    This ensures that at least the starting position can be re-used.
 
     See also
     --------