diff --git a/torchflows/bijections/finite/autoregressive/layers.py b/torchflows/bijections/finite/autoregressive/layers.py
index 6e2133a..be33e97 100644
--- a/torchflows/bijections/finite/autoregressive/layers.py
+++ b/torchflows/bijections/finite/autoregressive/layers.py
@@ -21,10 +21,9 @@ def __init__(self, event_shape, **kwargs):
         """
 
         :param Union[Tuple[int, ...], torch.Size] event_shape: shape of the event tensor.
-        :param kwargs: keyword arguments to Affine.
+        :param kwargs: keyword arguments to ElementwiseBijection.
         """
-        transformer = Affine(event_shape, **kwargs)
-        super().__init__(transformer)
+        super().__init__(event_shape, Affine, **kwargs)
 
 
 class ElementwiseInverseAffine(ElementwiseBijection):
@@ -32,10 +31,9 @@ def __init__(self, event_shape, **kwargs):
         """
 
         :param Union[Tuple[int, ...], torch.Size] event_shape: shape of the event tensor.
-        :param kwargs: keyword arguments to InverseAffine.
+        :param kwargs: keyword arguments to ElementwiseBijection.
         """
-        transformer = InverseAffine(event_shape, **kwargs)
-        super().__init__(transformer)
+        super().__init__(event_shape, InverseAffine, **kwargs)
 
 
 class ActNorm(ElementwiseInverseAffine):
@@ -75,20 +73,19 @@ def __init__(self, event_shape, **kwargs):
         """
 
         :param Union[Tuple[int, ...], torch.Size] event_shape: shape of the event tensor.
-        :param kwargs: keyword arguments to Scale.
+        :param kwargs: keyword arguments to ElementwiseBijection.
         """
-        transformer = Scale(event_shape, **kwargs)
-        super().__init__(transformer)
+        super().__init__(event_shape, Scale, **kwargs)
 
 
 class ElementwiseShift(ElementwiseBijection):
-    def __init__(self, event_shape):
+    def __init__(self, event_shape, **kwargs):
         """
 
         :param Union[Tuple[int, ...], torch.Size] event_shape: shape of the event tensor.
+        :param kwargs: keyword arguments to ElementwiseBijection.
         """
-        transformer = Shift(event_shape)
-        super().__init__(transformer)
+        super().__init__(event_shape, Shift, **kwargs)
 
 
 class ElementwiseRQSpline(ElementwiseBijection):
@@ -96,10 +93,9 @@ def __init__(self, event_shape, **kwargs):
         """
 
         :param Union[Tuple[int, ...], torch.Size] event_shape: shape of the event tensor.
-        :param kwargs: keyword arguments to RationalQuadratic.
+        :param kwargs: keyword arguments to ElementwiseBijection.
         """
-        transformer = RationalQuadratic(event_shape, **kwargs)
-        super().__init__(transformer)
+        super().__init__(event_shape, RationalQuadratic, **kwargs)
 
 
 class AffineCoupling(CouplingBijection):
diff --git a/torchflows/bijections/finite/autoregressive/layers_base.py b/torchflows/bijections/finite/autoregressive/layers_base.py
index 4e7cdc4..9a47270 100644
--- a/torchflows/bijections/finite/autoregressive/layers_base.py
+++ b/torchflows/bijections/finite/autoregressive/layers_base.py
@@ -1,4 +1,4 @@
-from typing import Tuple, Union, Type
+from typing import Tuple, Union, Type, Optional
 
 import torch
 import torch.nn as nn
@@ -15,7 +15,7 @@ class AutoregressiveBijection(Bijection):
     def __init__(self,
                  event_shape,
                  transformer: Union[TensorTransformer, ScalarTransformer],
-                 conditioner_transform: ConditionerTransform,
+                 conditioner_transform: Optional[ConditionerTransform],
                  **kwargs):
         super().__init__(event_shape=event_shape)
         self.conditioner_transform = conditioner_transform
@@ -205,11 +205,19 @@ class ElementwiseBijection(AutoregressiveBijection):
     The bijection for each element has its own set of globally learned parameters.
     """
 
-    def __init__(self, transformer: ScalarTransformer, fill_value: float = None):
+    def __init__(self,
+                 event_shape: Union[Tuple[int, ...], torch.Size],
+                 transformer_class: Type[ScalarTransformer],
+                 transformer_kwargs: dict = None,
+                 fill_value: float = None,
+                 **kwargs):
+        transformer_kwargs = transformer_kwargs or {}
+        transformer = transformer_class(event_shape=event_shape, **transformer_kwargs)
         super().__init__(
-            transformer.event_shape,
+            event_shape,
             transformer,
-            None
+            None,
+            **kwargs
         )
 
         if fill_value is None:
diff --git a/torchflows/bijections/finite/autoregressive/transformers/linear/affine.py b/torchflows/bijections/finite/autoregressive/transformers/linear/affine.py
index 5c0e9a4..13ab46e 100644
--- a/torchflows/bijections/finite/autoregressive/transformers/linear/affine.py
+++ b/torchflows/bijections/finite/autoregressive/transformers/linear/affine.py
@@ -135,7 +135,7 @@ def inverse(self, z: torch.Tensor, h: torch.Tensor) -> Tuple[torch.Tensor, torch
 
 
 class Shift(ScalarTransformer):
-    def __init__(self, event_shape: torch.Size):
+    def __init__(self, event_shape: torch.Size, **kwargs):
         super().__init__(event_shape=event_shape)
 
     @property