fix formatting clobber

torch_struct/distributions.py

@@ -90,7 +90,9 @@ def cross_entropy(self, other):
             cross entropy (*batch_shape*)
         """
 
-        return self._struct(CrossEntropySemiring).sum([self.log_potentials, other.log_potentials], self.lengths)
+        return self._struct(CrossEntropySemiring).sum(
+            [self.log_potentials, other.log_potentials], self.lengths
+        )
 
     def kl(self, other):
         """
@@ -99,7 +101,9 @@ def kl(self, other):
         Returns:
             cross entropy (*batch_shape*)
         """
-        return self._struct(KLDivergenceSemiring).sum([self.log_potentials, other.log_potentials], self.lengths)
+        return self._struct(KLDivergenceSemiring).sum(
+            [self.log_potentials, other.log_potentials], self.lengths
+        )
 
     @lazy_property
     def max(self):
@@ -127,7 +131,9 @@ def kmax(self, k):
             kmax (*k x batch_shape*)
         """
         with torch.enable_grad():
-            return self._struct(KMaxSemiring(k)).sum(self.log_potentials, self.lengths, _raw=True)
+            return self._struct(KMaxSemiring(k)).sum(
+                self.log_potentials, self.lengths, _raw=True
+            )
 
     def topk(self, k):
         r"""
@@ -137,7 +143,9 @@ def topk(self, k):
             kmax (*k x batch_shape x event_shape*)
         """
         with torch.enable_grad():
-            return self._struct(KMaxSemiring(k)).marginals(self.log_potentials, self.lengths, _raw=True)
+            return self._struct(KMaxSemiring(k)).marginals(
+                self.log_potentials, self.lengths, _raw=True
+            )
 
     @lazy_property
     def mode(self):
@@ -192,7 +200,9 @@ def sample(self, sample_shape=torch.Size()):
         samples = []
         for k in range(nsamples):
             if k % 10 == 0:
-                sample = self._struct(MultiSampledSemiring).marginals(self.log_potentials, lengths=self.lengths)
+                sample = self._struct(MultiSampledSemiring).marginals(
+                    self.log_potentials, lengths=self.lengths
+                )
                 sample = sample.detach()
             tmp_sample = MultiSampledSemiring.to_discrete(sample, (k % 10) + 1)
             samples.append(tmp_sample)
@@ -213,7 +223,9 @@ def enumerate_support(self, expand=True):
         Returns:
             (enum, enum_lengths) - (*tuple cardinality x batch_shape x event_shape*)
         """
-        _, _, edges, enum_lengths = self._struct().enumerate(self.log_potentials, self.lengths)
+        _, _, edges, enum_lengths = self._struct().enumerate(
+            self.log_potentials, self.lengths
+        )
         # if expand:
         #     edges = edges.unsqueeze(1).expand(edges.shape[:1] + self.batch_shape[:1] + edges.shape[1:])
         return edges, enum_lengths
@@ -284,7 +296,9 @@ def __init__(self, log_potentials, local=False, lengths=None, max_gap=None):
         super().__init__(log_potentials, lengths)
 
     def _struct(self, sr=None):
-        return self.struct(sr if sr is not None else LogSemiring, self.local, max_gap=self.max_gap)
+        return self.struct(
+            sr if sr is not None else LogSemiring, self.local, max_gap=self.max_gap
+        )
 
 
 class HMM(StructDistribution):
@@ -437,7 +451,9 @@ def __init__(self, log_potentials, lengths=None):
         event_shape = log_potentials[0].shape[1:]
         self.log_potentials = log_potentials
         self.lengths = lengths
-        super(StructDistribution, self).__init__(batch_shape=batch_shape, event_shape=event_shape)
+        super(StructDistribution, self).__init__(
+            batch_shape=batch_shape, event_shape=event_shape
+        )
 
 
 class NonProjectiveDependencyCRF(StructDistribution):