diff --git a/dwave/plugins/torch/nn/modules/categorical.py b/dwave/plugins/torch/nn/modules/categorical.py
new file mode 100755
index 0000000..d1cc059
--- /dev/null
+++ b/dwave/plugins/torch/nn/modules/categorical.py
@@ -0,0 +1,33 @@
+# Copyright 2025 D-Wave
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import torch
+from torch import nn
+
+
+class BinCat(nn.Module):
+    def __init__(self, length, dim):
+        super().__init__()
+        self.strings = torch.cartesian_prod(*torch.tensor([[0, 1],]*length))
+        self.indices = torch.arange(length)
+        self.cats = torch.nn.Parameter(torch.randn(2**length, dim))
+
+    def forward(self, x):
+        assert x.shape[-1] == self.indices.shape[0]
+        assert set(x.unique().tolist()).issubset({0, 1})
+        xcomp = torch.stack([x, 1-x], dim=-1)
+        xhot = xcomp[..., self.indices, self.strings].prod(-1)
+        assert (xhot.sum(-1) == 1).all()
+        cats = torch.einsum("b ... i k, k d -> b i d", xhot, self.cats)
+        return cats