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

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

src/scvi/external/sysvi/_base_components.py

@@ -317,8 +317,8 @@ def forward(self, x: torch.Tensor):
         # Force to be non nan - TODO come up with better way to do so
         if self.mode == "sample_feature":
             v = self.encoder(x)
-            v = (self.activation(v) + self.eps)  # Ensure that var is strictly positive
+            v = self.activation(v) + self.eps  # Ensure that var is strictly positive
         elif self.mode == "feature":
             v = self.var_param.expand(x.shape[0], -1)  # Broadcast to input size
-            v = (self.activation(v) + self.eps)  # Ensure that var is strictly positive
+            v = self.activation(v) + self.eps  # Ensure that var is strictly positive
         return v

src/scvi/external/sysvi/_model.py

@@ -11,8 +11,6 @@
 
 from scvi import REGISTRY_KEYS
 from scvi.data import AnnDataManager
-from scvi.data._constants import _SCVI_UUID_KEY
-from scvi.data._utils import _check_if_view
 from scvi.data.fields import (
     LayerField,
     ObsmField,
@@ -137,6 +135,7 @@ def get_latent_representation(
         return_dist
             If ``True``, returns the mean and variance of the latent distribution. Otherwise,
             returns the mean of the latent distribution.
+
         Returns
         -------
         Latent Embedding
@@ -192,8 +191,7 @@ def _validate_anndata(
 
         # Check that all required fields are present and match the Model's adata
         assert (
-            self.adata.uns["layer_information"]["layer"]
-            == adata.uns["layer_information"]["layer"]
+            self.adata.uns["layer_information"]["layer"] == adata.uns["layer_information"]["layer"]
         )
         assert (
             self.adata.uns["layer_information"]["var_names"]

src/scvi/external/sysvi/_module.py

@@ -350,7 +350,8 @@ def loss(
 
         # Reconstruction loss
         reconst_loss_x = torch.nn.GaussianNLLLoss(reduction="none")(
-            generative_outputs["x_m"], x_true, generative_outputs["x_v"]).sum(dim=1)
+            generative_outputs["x_m"], x_true, generative_outputs["x_v"]
+        ).sum(dim=1)
 
         reconst_loss = reconst_loss_x
 

tests/external/sysvi/test_model.py

@@ -171,5 +171,3 @@ def test_model():
                 give_mean=False,
             ),
         )
-
-