whitespace

python/lammps/mliap/pytorch.py

@@ -80,10 +80,10 @@ class TorchWrapper(torch.nn.Module):
 
             n_params : torch.nn.Module (None)
                 Number of NN model parameters
-            
+
             device : torch.nn.Module (None)
                 Accelerator device
-            
+
             dtype : torch.dtype (torch.float64)
                 Dtype to use on device
         """
@@ -325,6 +325,6 @@ class ElemwiseModels(torch.nn.Module):
         per_atom_attributes = torch.zeros(elems.size(dim=0), dtype=self.dtype)
         given_elems, elem_indices = torch.unique(elems, return_inverse=True)
         for i, elem in enumerate(given_elems):
-            self.subnets[elem].to(self.dtype) 
+            self.subnets[elem].to(self.dtype)
             per_atom_attributes[elem_indices == i] = self.subnets[elem](descriptors[elem_indices == i]).flatten()
         return per_atom_attributes