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

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

src/segger/models/segger_model.py

@@ -4,15 +4,19 @@
 from torch import Tensor
 from typing import Union
 
+
 class SkipGAT(nn.Module):
     def __init__(self, in_channels, out_channels, heads, apply_activation=True):
         super().__init__()
         self.apply_activation = apply_activation
-        self.conv = HeteroConv({
-            ('tx', 'neighbors', 'tx'): GATv2Conv(in_channels, out_channels, heads=heads, add_self_loops=False),
-            ('tx', 'belongs', 'bd'): GATv2Conv(in_channels, out_channels, heads=heads, add_self_loops=False),
-        }, aggr='sum')
-        self.lin = HeteroDictLinear(in_channels, out_channels * heads, types=('tx', 'bd'))
+        self.conv = HeteroConv(
+            {
+                ("tx", "neighbors", "tx"): GATv2Conv(in_channels, out_channels, heads=heads, add_self_loops=False),
+                ("tx", "belongs", "bd"): GATv2Conv(in_channels, out_channels, heads=heads, add_self_loops=False),
+            },
+            aggr="sum",
+        )
+        self.lin = HeteroDictLinear(in_channels, out_channels * heads, types=("tx", "bd"))
 
     def forward(self, x_dict, edge_index_dict):
         x_conv = self.conv(x_dict, edge_index_dict)
@@ -51,16 +55,20 @@ def __init__(
         # Initialize node embeddings
         if is_token_based:
             # Using token-based embeddings for transcript ('tx') nodes
-            self.node_init = nn.ModuleDict({
-                'tx': nn.Embedding(num_node_features['tx'], init_emb),
-                'bd': nn.Linear(num_node_features['bd'], init_emb),
-            })
+            self.node_init = nn.ModuleDict(
+                {
+                    "tx": nn.Embedding(num_node_features["tx"], init_emb),
+                    "bd": nn.Linear(num_node_features["bd"], init_emb),
+                }
+            )
         else:
             # Using scRNAseq embeddings (i.e. prior biological knowledge) for transcript ('tx') nodes
-            self.node_init = nn.ModuleDict({
-                'tx': nn.Linear(num_node_features['tx'], init_emb),
-                'bd': nn.Linear(num_node_features['bd'], init_emb),
-            })
+            self.node_init = nn.ModuleDict(
+                {
+                    "tx": nn.Linear(num_node_features["tx"], init_emb),
+                    "bd": nn.Linear(num_node_features["bd"], init_emb),
+                }
+            )
 
         # First GATv2Conv layer
         self.conv1 = SkipGAT(init_emb, hidden_channels, heads)
@@ -71,12 +79,12 @@ def __init__(
             self.conv_mid_layers = nn.ModuleList()
             for _ in range(num_mid_layers):
                 self.conv_mid_layers.append(SkipGAT(heads * hidden_channels, hidden_channels, heads))
-        
+
         # Last GATv2Conv layer
         self.conv_last = SkipGAT(heads * hidden_channels, out_channels, heads)
 
         # Finalize node embeddings
-        self.node_final = HeteroDictLinear(heads * out_channels, out_channels, types=('tx', 'bd'))
+        self.node_final = HeteroDictLinear(heads * out_channels, out_channels, types=("tx", "bd"))
 
         # # Edge probability predictor
         # self.edge_predictor = nn.Sequential(
@@ -113,10 +121,10 @@ def forward(
         return x_dict
 
     def decode(
-            self,
-            z_dict: dict[str, Tensor],
-            edge_index: Union[Tensor],
-        ) -> Tensor:
+        self,
+        z_dict: dict[str, Tensor],
+        edge_index: Union[Tensor],
+    ) -> Tensor:
         """
         Decode the node embeddings to predict edge values.
 
@@ -127,7 +135,7 @@ def decode(
         Returns:
             Tensor: Predicted edge values.
         """
-        z_left = z_dict['tx'][edge_index[0]]
-        z_right = z_dict['bd'][edge_index[1]]
+        z_left = z_dict["tx"][edge_index[0]]
+        z_right = z_dict["bd"][edge_index[1]]
         return (z_left * z_right).sum(dim=-1)
         # return self.edge_predictor(torch.cat([z_left, z_right], dim=-1)).squeeze()