[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/scvi/external/decipher/_model.py

@@ -75,9 +75,7 @@ def setup_anndata(
         anndata_fields = [
             LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
         ]
-        adata_manager = AnnDataManager(
-            fields=anndata_fields, setup_method_args=setup_method_args
-        )
+        adata_manager = AnnDataManager(fields=anndata_fields, setup_method_args=setup_method_args)
         adata_manager.register_fields(adata, **kwargs)
         cls.register_manager(adata_manager)
 
@@ -140,9 +138,7 @@ def get_latent_representation(
         self._check_if_trained(warn=False)
         adata = self._validate_anndata(adata)
 
-        scdl = self._make_data_loader(
-            adata=adata, indices=indices, batch_size=batch_size
-        )
+        scdl = self._make_data_loader(adata=adata, indices=indices, batch_size=batch_size)
         latent_locs = []
         for tensors in scdl:
             x = tensors[REGISTRY_KEYS.X_KEY]

src/scvi/external/decipher/_module.py

@@ -78,9 +78,7 @@ def device(self):
         return self._dummy_param.device
 
     @staticmethod
-    def _get_fn_args_from_batch(
-        tensor_dict: dict[str, torch.Tensor]
-    ) -> Iterable | dict:
+    def _get_fn_args_from_batch(tensor_dict: dict[str, torch.Tensor]) -> Iterable | dict:
         x = tensor_dict[REGISTRY_KEYS.X_KEY]
         return (x,), {}
 
@@ -116,9 +114,7 @@ def model(self, x: torch.Tensor):
                 self.theta + self._epsilon
             )
             # noinspection PyUnresolvedReferences
-            x_dist = dist.NegativeBinomial(
-                total_count=self.theta + self._epsilon, logits=logit
-            )
+            x_dist = dist.NegativeBinomial(total_count=self.theta + self._epsilon, logits=logit)
             pyro.sample("x", x_dist.to_event(1), obs=x)
 
     @auto_move_data
@@ -174,9 +170,7 @@ def predictive_log_likelihood(self, x: torch.Tensor, n_samples=5):
                 model_trace = poutine.trace(
                     poutine.replay(self.model, trace=guide_trace)
                 ).get_trace(x)
-                log_weights.append(
-                    model_trace.log_prob_sum() - guide_trace.log_prob_sum()
-                )
+                log_weights.append(model_trace.log_prob_sum() - guide_trace.log_prob_sum())
 
         finally:
             self.beta = old_beta