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

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

test/nn/models/test_neural_fingerprint.py

@@ -3,12 +3,14 @@
 
 from torch_geometric.nn import NeuralFingerprint
 
+
 def test_neural_fingerprint():
-    x = torch.randn(3,7)
-    edge_index = torch.tensor([[0,1,1,2],[1,0,2,1]])
-    model = NeuralFingerprint(num_features=7, fingerprint_length=5, num_layers=4)
+    x = torch.randn(3, 7)
+    edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
+    model = NeuralFingerprint(num_features=7, fingerprint_length=5,
+                              num_layers=4)
     fingerprint = model.forward(x, edge_index)
     assert fingerprint.size() == torch.Size([5])
 
-test_neural_fingerprint()
 
+test_neural_fingerprint()

torch_geometric/nn/models/neural_fingerprint.py

@@ -1,10 +1,11 @@
 import torch
-from torch import tensor
 import torch.nn.functional as F
+from torch import tensor
 
 from torch_geometric.nn.conv import MFConv
 from torch_geometric.nn.dense import Linear
 
+
 class NeuralFingerprint(torch.nn.Module):
     def __init__(self, num_features, fingerprint_length, num_layers):
         super().__init__()
@@ -13,16 +14,19 @@ def __init__(self, num_features, fingerprint_length, num_layers):
         self.num_layers = num_layers
         self.layers = torch.nn.ModuleList()
         for i in range(self.num_layers):
-            self.layers.append(MFConv(in_channels=self.num_features, out_channels=self.num_features))
-            self.layers.append(Linear(in_channels=self.num_features, out_channels=self.fingerprint_length))
-
+            self.layers.append(
+                MFConv(in_channels=self.num_features,
+                       out_channels=self.num_features))
+            self.layers.append(
+                Linear(in_channels=self.num_features,
+                       out_channels=self.fingerprint_length))
+
     def forward(self, x, edge_index):
         fingerprint = torch.zeros(self.fingerprint_length)
-        for i in range(0,2*self.num_layers,2):
+        for i in range(0, 2 * self.num_layers, 2):
             x = self.layers[i](x, edge_index)
             x = torch.sigmoid(x)
-            y = F.softmax(self.layers[i+1](x), dim=1)
+            y = F.softmax(self.layers[i + 1](x), dim=1)
             for j in range(y.shape[0]):
                 fingerprint += y[j]
         return fingerprint
-