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

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torch_geometric/nn/rearev.py

@@ -1,10 +1,12 @@
 import torch
 import torch.nn as nn
 from torch.autograd import Variable
-from reason import 
+
 from torch_geometric.nn import GlobalAttention
 from torch_geometric.utils.reason import Fusion, QueryReform, TypeLayer
 
+from reason import
+
 
 class ReaRev(torch.nn.Module):
     """Recurrent Attention and Reasoning model (ReaRev) for iterative reasoning
@@ -47,7 +49,7 @@ def _define_layers(self, args):
         self.layers(args)
 
     def layers(self, args):
-        """Define layers for embedding transformations 
+        """Define layers for embedding transformations
         and attention encoders."""
         self.word_dim
         self.kg_dim
@@ -138,7 +140,7 @@ def init_reason(self, curr_dist, local_entity, kb_adj_mat, q_input,
         )
 
     def calc_loss_label(self, curr_dist, teacher_dist, label_valid):
-        """Computes the label loss based on current predictions vs. 
+        """Computes the label loss based on current predictions vs.
         teacher distribution.
         """
         tp_loss = self.get_loss(pred_dist=curr_dist, answer_dist=teacher_dist,
@@ -148,10 +150,10 @@ def calc_loss_label(self, curr_dist, teacher_dist, label_valid):
         return cur_loss
 
     def forward(self, batch, training=False):
-        """Standard forward pass: perform reasoning steps and 
+        """Standard forward pass: perform reasoning steps and
         produce predictions and loss.
         """
-        local_entity, query_entities, kb_adj_mat, query_text, 
+        local_entity, query_entities, kb_adj_mat, query_text,
         seed_dist, _, answer_dist = batch
         local_entity = torch.from_numpy(local_entity).long().to(self.device)
         query_entities = torch.from_numpy(query_entities).float().to(