diff --git a/FrEIA/modules/all_in_one_block.py b/FrEIA/modules/all_in_one_block.py
index 0a74570..50a6ee6 100644
--- a/FrEIA/modules/all_in_one_block.py
+++ b/FrEIA/modules/all_in_one_block.py
@@ -239,6 +239,9 @@ def _affine(self, x, a, rev=False):
 
     def forward(self, x, c=[], rev=False, jac=True):
         '''See base class docstring'''
+        if x.shape[1:] != self.dims_in[0][1:]:
+            raise RuntimeError(f"Expected input of shape {self.dims_in[0]}, "
+                             f"got {x.shape}.")
         if self.householder:
             self.w_perm = self._construct_householder_permutation()
             if rev or self.reverse_pre_permute:
diff --git a/FrEIA/modules/splines/binned.py b/FrEIA/modules/splines/binned.py
index 44d46fb..ad20009 100644
--- a/FrEIA/modules/splines/binned.py
+++ b/FrEIA/modules/splines/binned.py
@@ -94,19 +94,14 @@ def __init__(self, dims_in, dims_c=None, bins: int = 10, parameter_counts: Dict[
         assert default_domain[3] - default_domain[2] >= min_bin_sizes[1] * bins, \
         "{bins} bins of size {min_bin_sizes[1]} are too large for domain {default_domain[2]} to {default_domain[3]}"
 
-        if domain_clamping is not None:
-            self.clamp_domain = lambda domain: domain_clamping * torch.tanh(
-                domain / domain_clamping
-            )
-        else:
-            self.clamp_domain = lambda domain: domain
-
         self.register_buffer("bins", torch.tensor(bins, dtype=torch.int32))
         self.register_buffer("min_bin_sizes", torch.as_tensor(min_bin_sizes, dtype=torch.float32))
         self.register_buffer("default_domain", torch.as_tensor(default_domain, dtype=torch.float32))
         self.register_buffer("identity_tails", torch.tensor(identity_tails, dtype=torch.bool))
         self.register_buffer("default_width", torch.as_tensor(default_domain[1] - default_domain[0], dtype=torch.float32))
 
+        self.domain_clamping = domain_clamping
+
         # The default parameters are
         #       parameter                                       constraints             count
         # 1.    the leftmost bin edge                           -                       1
@@ -140,6 +135,15 @@ def split_parameters(self, parameters: torch.Tensor, split_len: int) -> Dict[str
 
         return dict(zip(keys, values))
 
+    def clamp_domain(self, domain: torch.Tensor) -> torch.Tensor:
+        """
+        Clamp domain to the a size between (-domain_clamping, domain_clamping)
+        """
+        if self.domain_clamping is None:
+            return domain
+        else:
+            return self.domain_clamping * torch.tanh(domain / self.domain_clamping)
+
     def constrain_parameters(self, parameters: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
         """
         Constrain Parameters to meet certain conditions (e.g. positivity)
diff --git a/FrEIA/modules/splines/rational_quadratic.py b/FrEIA/modules/splines/rational_quadratic.py
index 51b5fea..e8673d0 100644
--- a/FrEIA/modules/splines/rational_quadratic.py
+++ b/FrEIA/modules/splines/rational_quadratic.py
@@ -164,7 +164,8 @@ def rational_quadratic_spline(x: torch.Tensor,
 
         # Eq 29 in the appendix of the paper
         discriminant = b ** 2 - 4 * a * c
-        assert torch.all(discriminant >= 0), f"Discriminant must be positive, but is violated by {torch.min(discriminant)}"
+        if not torch.all(discriminant >= 0):
+            raise(RuntimeError(f"Discriminant must be positive, but is violated by {torch.min(discriminant)}"))
 
         xi = 2 * c / (-b - torch.sqrt(discriminant))