Make black happy

Author: AustinRochford

pymc/distributions/discrete.py

@@ -1745,8 +1745,7 @@ class OrderedLogistic:
     def __new__(cls, name, *args, compute_p=True, **kwargs):
         out_rv = _OrderedLogistic(name, *args, **kwargs)
         if compute_p:
-            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[3],
-                             dims=kwargs.get('dims'))
+            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[3], dims=kwargs.get('dims'))
         return out_rv
 
     @classmethod
@@ -1857,8 +1856,7 @@ class OrderedProbit:
     def __new__(cls, name, *args, compute_p=True, **kwargs):
         out_rv = _OrderedProbit(name, *args, **kwargs)
         if compute_p:
-            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[3],
-                             dims=kwargs.get('dims'))
+            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[3], dims=kwargs.get('dims'))
         return out_rv
 
     @classmethod

pymc/distributions/multivariate.py

@@ -752,8 +752,7 @@ class OrderedMultinomial:
     def __new__(cls, name, *args, compute_p=True, **kwargs):
         out_rv = _OrderedMultinomial(name, *args, **kwargs)
         if compute_p:
-            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[4],
-                             dims=kwargs.get('dims'))
+            pm.Deterministic(f"{name}_probs", out_rv.owner.inputs[4], dims=kwargs.get('dims'))
         return out_rv
 
     @classmethod