run precommit

pymc_experimental/distributions/discrete.py

@@ -171,19 +171,20 @@ def logp(value, mu, lam):
             (-mu / 4) <= lam,
             msg="0 < mu, max(-1, -mu/4)) <= lam <= 1",
         )
-
+
+
 class BetaNegativeBinomial:
     R"""
     Beta Negative Binomial distribution.
-    
+
     The pmf of this distribution is
 
     .. math::
 
         f(x \mid \alpha, \beta, r) = \frac{B(r + x, \alpha + \beta)}{B(r, \alpha)} \frac{\Gamma(x + \beta)}{x! \Gamma(\beta)}
 
     where :math:`B` is the Beta function and :math:`\Gamma` is the Gamma function.
-    
+
     .. plot::
         :context: close-figs
 
@@ -216,7 +217,7 @@ def logp(x, alpha, beta, r):
         plt.ylabel('f(x)', fontsize=12)
         plt.legend(loc=1)
         plt.show()
-        
+
     For more information, see https://en.wikipedia.org/wiki/Beta_negative_binomial_distribution.
 
     ========  ======================================
@@ -273,9 +274,9 @@ def __new__(cls, name, alpha, beta, r, **kwargs):
             dist=cls.beta_negative_binomial_dist,
             logp=cls.beta_negative_binomial_logp,
             class_name="BetaNegativeBinomial",
-            **kwargs 
+            **kwargs,
         )
-      
+
     @classmethod
     def dist(cls, alpha, beta, r, **kwargs):
         return pm.CustomDist.dist(
@@ -285,10 +286,10 @@ def dist(cls, alpha, beta, r, **kwargs):
             dist=cls.beta_negative_binomial_dist,
             logp=cls.beta_negative_binomial_logp,
             class_name="BetaNegativeBinomial",
-          **kwargs
+            **kwargs,
         )
 
-          
+
 class Skellam:
     R"""
     Skellam distribution.

pymc_experimental/tests/distributions/test_discrete.py

@@ -29,7 +29,11 @@
 )
 from pytensor import config
 
-from pymc_experimental.distributions import BetaNegativeBinomial, GeneralizedPoisson, Skellam
+from pymc_experimental.distributions import (
+    BetaNegativeBinomial,
+    GeneralizedPoisson,
+    Skellam,
+)
 
 
 class TestGeneralizedPoisson:
@@ -121,7 +125,7 @@ def test_moment(self, mu, lam, size, expected):
             GeneralizedPoisson("x", mu=mu, lam=lam, size=size)
         assert_moment_is_expected(model, expected)
 
-        
+
 class TestBetaNegativeBinomial:
     """
     Wrapper class so that tests of experimental additions can be dropped into
@@ -189,8 +193,8 @@ def test_logp(self):
             np.testing.assert_allclose(
                 logp_fn(test_value, test_alpha, test_beta, test_r), expected_logp
             )
-           
-          
+
+
 class TestSkellam:
     def test_logp(self):
         check_logp(