Add options to docstring

sbi/inference/npse/npse.py

@@ -56,7 +56,8 @@ def __init__(
         Args:
             prior: Prior distribution.
             score_estimator: Neural network architecture for the score estimator. Can be
-                a string (e.g. 'mlp') or a callable that returns a neural network.
+                a string (e.g. 'mlp' or 'ada_mlp') or a callable that returns a neural
+                network.
             sde_type: Type of SDE to use. Must be one of ['vp', 've', 'subvp'].
             device: Device to run the training on.
             logging_level: Logging level for the training. Can be an integer or a

sbi/inference/potentials/score_based_potential.py

@@ -162,7 +162,7 @@ def gradient(
                 raise NotImplementedError(
                     "Score accumulation for IID data is not yet implemented."
                 )
-               
+
         return score
 
     def get_continuous_normalizing_flow(
@@ -229,4 +229,3 @@ def f(t, x):
         exact=exact,
     )
     return transform
-

sbi/samplers/score/predictors.py

@@ -120,4 +120,3 @@ def predict(self, theta: Tensor, t1: Tensor, t0: Tensor):
         f_backward = f - (1 + self.eta**2) / 2 * g**2 * score
         g_backward = self.eta * g
         return theta - f_backward * dt + g_backward * torch.randn_like(theta) * dt_sqrt
-

tests/score_samplers_test.py

@@ -61,7 +61,6 @@ def _build_gaussian_score_estimator(
     # Note the precondition predicts a correct Gaussian score by default if the neural
     # net predicts 0!
     class DummyNet(torch.nn.Module):
-
         def __init__(self):
             super().__init__()
             self.dummy_param_for_device_detection = torch.nn.Linear(1, 1)