small fixes: sbc warnings, sample defaults, docs.

sbi/analysis/conditional_density.py

@@ -211,7 +211,7 @@ def __init__(
         )
         self.prec = self.precfs.transpose(3, 2) @ self.precfs
 
-    def sample(self, sample_shape: Shape) -> Tensor:
+    def sample(self, sample_shape: Shape = torch.Size()) -> Tensor:
         num_samples = torch.Size(sample_shape).numel()
         samples = mdn.sample_mog(num_samples, self.logits, self.means, self.precfs)
         return samples.detach().reshape((*sample_shape, -1))

sbi/analysis/sbc.py

@@ -53,16 +53,16 @@ def run_sbc(
     """
     num_sbc_samples = thetas.shape[0]
 
-    if num_sbc_samples < 1000:
+    if num_sbc_samples < 100:
         warnings.warn(
             """Number of SBC samples should be on the order of 100s to give realiable
-            results. We recommend using 300.""",
+            results.""",
             stacklevel=2,
         )
     if num_posterior_samples < 100:
         warnings.warn(
             """Number of posterior samples for ranking should be on the order
-            of 100s to give reliable SBC results. We recommend using at least 300.""",
+            of 100s to give reliable SBC results.""",
             stacklevel=2,
         )
 

sbi/inference/base.py

@@ -206,8 +206,9 @@ def append_simulations(
         from.
 
         Args:
-            theta: Parameter sets. x: Simulation outputs. exclude_invalid_x: Whether
-            invalid simulations are discarded during
+            theta: Parameter sets.
+            x: Simulation outputs.
+            exclude_invalid_x: Whether invalid simulations are discarded during
                 training. If `False`, The inference algorithm raises an error when
                 invalid simulations are found. If `True`, invalid simulations are
                 discarded and training can proceed, but this gives systematically wrong

sbi/inference/posteriors/vi_posterior.py

@@ -615,8 +615,7 @@ def __setstate__(self, state_dict: Dict):
         e.g. remains deep copy compatible.
 
         Args:
-            state_dict (Dict): Given state dictionary, we will restore the object from
-            it.
+            state_dict: Given state dictionary, we will restore the object from it.
         """
         self.__dict__ = state_dict
         q = deepcopy(self._q)

sbi/utils/conditional_density_utils.py

@@ -294,15 +294,12 @@ def __init__(
         self.device = self.potential_fn.device
         self.allow_iid_x = allow_iid_x
 
-    def __call__(
-        self, theta: Tensor, x_o: Tensor, track_gradients: bool = True
-    ) -> Tensor:
+    def __call__(self, theta: Tensor, track_gradients: bool = True) -> Tensor:
         r"""
         Returns the conditional potential $\log(p(\theta_i|\theta_j, x))$.
 
         Args:
             theta: Free parameters $\theta_i$, batch dimension 1.
-            x_o: Observed data $x$.
             track_gradients: Whether to track gradients.
 
         Returns: