delete all python2 style super calls (#14) (#540)

* delete all python2 style super calls

* flake8
Co-authored-by: Ignacio Pickering <ign.pickering@gmail.com>

torchani/ase.py

@@ -27,7 +27,7 @@ class Calculator(ase.calculators.calculator.Calculator):
     implemented_properties = ['energy', 'forces', 'stress', 'free_energy']
 
     def __init__(self, species, model, overwrite=False):
-        super(Calculator, self).__init__()
+        super().__init__()
         self.species_to_tensor = utils.ChemicalSymbolsToInts(species)
         self.model = model
         self.overwrite = overwrite
@@ -46,7 +46,7 @@ class Calculator(ase.calculators.calculator.Calculator):
 
     def calculate(self, atoms=None, properties=['energy'],
                   system_changes=ase.calculators.calculator.all_changes):
-        super(Calculator, self).calculate(atoms, properties, system_changes)
+        super().calculate(atoms, properties, system_changes)
         cell = torch.tensor(self.atoms.get_cell(complete=True),
                             dtype=self.dtype, device=self.device)
         pbc = torch.tensor(self.atoms.get_pbc(), dtype=torch.bool,

torchani/models.py

@@ -45,7 +45,7 @@ class BuiltinModel(torch.nn.Module):
     r"""Private template for the builtin ANI models """
 
     def __init__(self, species_converter, aev_computer, neural_networks, energy_shifter, species_to_tensor, consts, sae_dict, periodic_table_index):
-        super(BuiltinModel, self).__init__()
+        super().__init__()
         self.species_converter = species_converter
         self.aev_computer = aev_computer
         self.neural_networks = neural_networks
@@ -221,13 +221,8 @@ class BuiltinEnsemble(BuiltinModel):
 
     def __init__(self, species_converter, aev_computer, neural_networks,
                  energy_shifter, species_to_tensor, consts, sae_dict, periodic_table_index):
-        super(BuiltinEnsemble, self).__init__(species_converter,
-                                              aev_computer,
-                                              neural_networks,
-                                              energy_shifter,
-                                              species_to_tensor,
-                                              consts,
-                                              sae_dict,
+        super().__init__(species_converter, aev_computer, neural_networks,
+                         energy_shifter, species_to_tensor, consts, sae_dict,
                          periodic_table_index)
 
     @classmethod

torchani/nn.py

@@ -49,7 +49,7 @@ class ANIModel(torch.nn.ModuleDict):
         return od
 
     def __init__(self, modules):
-        super(ANIModel, self).__init__(self.ensureOrderedDict(modules))
+        super().__init__(self.ensureOrderedDict(modules))
 
     def forward(self, species_aev: Tuple[Tensor, Tensor],  # type: ignore
                 cell: Optional[Tensor] = None,
@@ -93,7 +93,7 @@ class Sequential(torch.nn.ModuleList):
     """Modified Sequential module that accept Tuple type as input"""
 
     def __init__(self, *modules):
-        super(Sequential, self).__init__(modules)
+        super().__init__(modules)
 
     def forward(self, input_: Tuple[Tensor, Tensor],  # type: ignore
                 cell: Optional[Tensor] = None,

torchani/utils.py

@@ -151,7 +151,7 @@ class EnergyShifter(torch.nn.Module):
     """
 
     def __init__(self, self_energies, fit_intercept=False):
-        super(EnergyShifter, self).__init__()
+        super().__init__()
 
         self.fit_intercept = fit_intercept
         if self_energies is not None: