diff --git a/simple_einet/data.py b/simple_einet/data.py
index 2a5d1f1..8cfc60c 100644
--- a/simple_einet/data.py
+++ b/simple_einet/data.py
@@ -26,6 +26,7 @@
 )
 
 from simple_einet.layers.distributions.binomial import Binomial
+from simple_einet.layers.distributions.bernoulli import Bernoulli
 from simple_einet.layers.distributions.categorical import Categorical
 from simple_einet.layers.distributions.multivariate_normal import MultivariateNormal
 from simple_einet.layers.distributions.normal import Normal, RatNormal
@@ -525,6 +526,7 @@ class Dist(str, Enum):
     NORMAL_RAT = "normal_rat"
     BINOMIAL = "binomial"
     CATEGORICAL = "categorical"
+    BERNOULLI = "bernoulli"
 
 
 def get_distribution(dist: Dist, cfg):
@@ -554,6 +556,9 @@ def get_distribution(dist: Dist, cfg):
     elif dist == Dist.MULTIVARIATE_NORMAL:
         leaf_type = MultivariateNormal
         leaf_kwargs = {"cardinality": cfg.multivariate_cardinality}
+    elif dist == Dist.BERNOULLI:
+        leaf_type = Bernoulli
+        leaf_kwargs = {}
     else:
         raise ValueError(f"Unknown distribution ({dist}).")
     return leaf_kwargs, leaf_type
diff --git a/simple_einet/layers/distributions/bernoulli.py b/simple_einet/layers/distributions/bernoulli.py
index 7b52e05..27d4104 100644
--- a/simple_einet/layers/distributions/bernoulli.py
+++ b/simple_einet/layers/distributions/bernoulli.py
@@ -1,4 +1,5 @@
 import torch
+from simple_einet.sampling_utils import SamplingContext
 from torch import distributions as dist
 from torch import nn
 
@@ -26,6 +27,6 @@ def __init__(self, num_features: int, num_channels: int, num_leaves: int, num_re
         # Create bernoulli parameters
         self.probs = nn.Parameter(torch.randn(1, num_channels, num_features, num_leaves, num_repetitions))
 
-    def _get_base_distribution(self):
+    def _get_base_distribution(self, ctx: SamplingContext = None):
         # Use sigmoid to ensure, that probs are in valid range
         return dist.Bernoulli(probs=torch.sigmoid(self.probs))