diff --git a/normalizing_flows/bijections/finite/autoregressive/layers_base.py b/normalizing_flows/bijections/finite/autoregressive/layers_base.py
index 83fa848..fab59fe 100644
--- a/normalizing_flows/bijections/finite/autoregressive/layers_base.py
+++ b/normalizing_flows/bijections/finite/autoregressive/layers_base.py
@@ -1,6 +1,7 @@
 from typing import Tuple, Optional, Union
 
 import torch
+import torch.nn as nn
 
 from normalizing_flows.bijections.finite.autoregressive.conditioners.base import Conditioner, NullConditioner
 from normalizing_flows.bijections.finite.autoregressive.conditioner_transforms import ConditionerTransform, Constant, \
@@ -160,7 +161,24 @@ class ElementwiseBijection(AutoregressiveBijection):
     def __init__(self, transformer: ScalarTransformer, fill_value: float = None):
         super().__init__(
             transformer.event_shape,
-            NullConditioner(),
+            None,
             transformer,
-            Constant(transformer.event_shape, transformer.parameter_shape, fill_value=fill_value)
+            None
         )
+
+        if fill_value is None:
+            self.value = nn.Parameter(torch.randn(*transformer.parameter_shape))
+        else:
+            self.value = nn.Parameter(torch.full(size=transformer.parameter_shape, fill_value=fill_value))
+
+    def prepare_h(self, batch_shape):
+        tmp = self.value[[None] * len(batch_shape)]
+        return tmp.repeat(*batch_shape, *([1] * len(self.transformer.parameter_shape)))
+
+    def forward(self, x: torch.Tensor, context: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        h = self.prepare_h(get_batch_shape(x, self.event_shape))
+        return self.transformer.forward(x, h)
+
+    def inverse(self, z: torch.Tensor, context: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        h = self.prepare_h(get_batch_shape(z, self.event_shape))
+        return self.transformer.inverse(z, h)