fix: run pre-commit

src/anemoi/models/graph/__init__.py

@@ -1,15 +1,21 @@
 from anemoi.utils.config import DotDict
 
+
 class AnemoiGraphSchema:
     def __init__(self, graph_data: dict, config: DotDict) -> None:
         self.hidden_name = config.graphs.hidden_mesh.name
         self.mesh_names = [name for name in graph_data if isinstance(name, str)]
-        self.input_meshes = [k[0] for k in graph_data if isinstance(k, tuple) and k[2] == self.hidden_name and k[2] != k[0]]
-        self.output_meshes = [k[2] for k in graph_data if isinstance(k, tuple) and k[0] == self.hidden_name and k[2] != k[0]]
+        self.input_meshes = [
+            k[0] for k in graph_data if isinstance(k, tuple) and k[2] == self.hidden_name and k[2] != k[0]
+        ]
+        self.output_meshes = [
+            k[2] for k in graph_data if isinstance(k, tuple) and k[0] == self.hidden_name and k[2] != k[0]
+        ]
         self.num_nodes = {name: graph_data[name]["coords"].shape[0] for name in self.mesh_names}
         self.num_node_features = {name: 2 * graph_data[name]["coords"].shape[1] for name in self.mesh_names}
         self.num_trainable_params = {
-            name: config.model.trainable_parameters["data" if name != "hidden" else name] for name in self.graph.mesh_names
+            name: config.model.trainable_parameters["data" if name != "hidden" else name]
+            for name in self.graph.mesh_names
         }
 
     def get_node_emb_size(self, name: str) -> int:

src/anemoi/models/models/encoder_processor_decoder.py

@@ -21,8 +21,8 @@
 
 from anemoi.models.data_indices.collection import IndexCollection
 from anemoi.models.distributed.shapes import get_shape_shards
-from anemoi.models.layers.graph import TrainableTensor
 from anemoi.models.graph import AnemoiGraphSchema
+from anemoi.models.layers.graph import TrainableTensor
 
 LOGGER = logging.getLogger(__name__)
 
@@ -196,7 +196,9 @@ def forward(self, x: Tensor, model_comm_group: Optional[ProcessGroup] = None) ->
                 dim=-1,  # feature dimension
             )
 
-        x_hidden_latent = self.trainable_tensors[self.graph.hidden_name](getattr(self, f"latlons_{self.graph.hidden_name}"), batch_size=batch_size)
+        x_hidden_latent = self.trainable_tensors[self.graph.hidden_name](
+            getattr(self, f"latlons_{self.graph.hidden_name}"), batch_size=batch_size
+        )
 
         # get shard shapes
         shard_shapes_data = {name: get_shape_shards(data, 0, model_comm_group) for name, data in x_data_latent.items()}