diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index 286e508bf2..8034185283 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -1626,7 +1626,7 @@ def support_point(rv, size, b, kappa, mu):
     def logp(value, b, kappa, mu):
         value = value - mu
         res = pt.log(b / (kappa + (kappa**-1))) + (
-            -value * b * pt.sgn(value) * (kappa ** pt.sgn(value))
+            -value * b * pt.sign(value) * (kappa ** pt.sign(value))
         )
 
         return check_parameters(
diff --git a/pymc/distributions/moments/means.py b/pymc/distributions/moments/means.py
index 450a90f8a6..f025733726 100644
--- a/pymc/distributions/moments/means.py
+++ b/pymc/distributions/moments/means.py
@@ -50,7 +50,7 @@
     UniformRV,
     VonMisesRV,
 )
-from pytensor.tensor.var import TensorVariable
+from pytensor.tensor.variable import TensorVariable
 
 from pymc.distributions.continuous import (
     AsymmetricLaplaceRV,
diff --git a/pymc/distributions/shape_utils.py b/pymc/distributions/shape_utils.py
index efd8d1f778..dcfb2e3d5b 100644
--- a/pymc/distributions/shape_utils.py
+++ b/pymc/distributions/shape_utils.py
@@ -461,7 +461,7 @@ def implicit_size_from_params(
     for param, ndim in zip(params, ndims_params):
         batch_shape = list(param.shape[:-ndim] if ndim > 0 else param.shape)
         # Overwrite broadcastable dims
-        for i, broadcastable in enumerate(param.type.broadcastable):
+        for i, broadcastable in enumerate(param.type.broadcastable[: len(batch_shape)]):
             if broadcastable:
                 batch_shape[i] = 1
         batch_shapes.append(batch_shape)
diff --git a/tests/distributions/test_shape_utils.py b/tests/distributions/test_shape_utils.py
index 493d7cb8c6..58f75e1cb8 100644
--- a/tests/distributions/test_shape_utils.py
+++ b/tests/distributions/test_shape_utils.py
@@ -35,9 +35,11 @@
     convert_size,
     get_support_shape,
     get_support_shape_1d,
+    implicit_size_from_params,
     rv_size_is_none,
 )
 from pymc.model import Model
+from pymc.pytensorf import constant_fold
 
 test_shapes = [
     ((), (1,), (4,), (5, 4)),
@@ -630,3 +632,10 @@ def test_get_support_shape(
             assert (f() == expected_support_shape).all()
             with pytest.raises(AssertionError, match="support_shape does not match"):
                 inferred_support_shape.eval()
+
+
+def test_implicit_size_from_params():
+    x = pt.tensor(shape=(5, 1))
+    y = pt.tensor(shape=(3, 3))
+    res = implicit_size_from_params(x, y, ndims_params=[1, 2])
+    assert constant_fold([res]) == (5,)