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

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deepmd/pt/model/task/ener.py

@@ -86,7 +86,7 @@ def __init__(
         self.activation_function = activation_function
         self.precision = precision
         self.prec = PRECISION_DICT[self.precision]
-        
+
         # init constants
         if self.numb_fparam > 0:
             self.register_buffer(
@@ -218,7 +218,6 @@ def _foward_common(
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
     ):
-
         xx = descriptor
         nf, nloc, nd = xx.shape
         if hasattr(self, "bias_atom_e"):
@@ -282,7 +281,11 @@ def _foward_common(
                 for type_i, filter_layer in enumerate(self.filter_layers_old):
                     mask = atype == type_i
                     atom_property = filter_layer(xx)
-                    atom_property = atom_property + self.bias_atom_e[type_i] if hasattr(self, "bias_atom_e") else atom_property
+                    atom_property = (
+                        atom_property + self.bias_atom_e[type_i]
+                        if hasattr(self, "bias_atom_e")
+                        else atom_property
+                    )
                     atom_property = atom_property * mask.unsqueeze(-1)
                     outs = outs + atom_property  # Shape is [nframes, natoms[0], 1]
             return {self.var_name: outs.to(env.GLOBAL_PT_FLOAT_PRECISION)}
@@ -297,11 +300,15 @@ def _foward_common(
                     mask = (atype == type_i).unsqueeze(-1)
                     mask = torch.tile(mask, (1, 1, self.dim_out))
                     atom_property = ll(xx)
-                    atom_property = atom_property + self.bias_atom_e[type_i] if hasattr(self, "bias_atom_e") else atom_property
+                    atom_property = (
+                        atom_property + self.bias_atom_e[type_i]
+                        if hasattr(self, "bias_atom_e")
+                        else atom_property
+                    )
                     atom_property = atom_property * mask
                     outs = outs + atom_property  # Shape is [nframes, natoms[0], 1]
             return {self.var_name: outs.to(env.GLOBAL_PT_FLOAT_PRECISION)}
-        
+
 
 @fitting_check_output
 class InvarFitting(GeneralFitting):
@@ -330,14 +337,14 @@ def __init__(
         - bias_atom_e: Average enery per atom for each element.
         - resnet_dt: Using time-step in the ResNet construction.
         """
-        super().__init__() 
+        super().__init__()
         if bias_atom_e is None:
             bias_atom_e = np.zeros([self.ntypes, self.dim_out])
         bias_atom_e = torch.tensor(bias_atom_e, dtype=self.prec, device=device)
         bias_atom_e = bias_atom_e.view([self.ntypes, self.dim_out])
         if not self.use_tebd:
             assert self.ntypes == bias_atom_e.shape[0], "Element count mismatches!"
-        self.register_buffer("bias_atom_e", bias_atom_e)      
+        self.register_buffer("bias_atom_e", bias_atom_e)
 
     def output_def(self) -> FittingOutputDef:
         return FittingOutputDef(
@@ -434,7 +441,6 @@ def forward(
         - `torch.Tensor`: Total energy with shape [nframes, natoms[0]].
         """
         return super().forward(descriptor, atype, gr, g2, h2, fparam, aparam)
-
 
 
 @Fitting.register("ener")