Ignore explicit size or shape of None

Author: ricardoV94

pymc/distributions/distribution.py

@@ -281,7 +281,7 @@ def __new__(
 
         # Preference is given to size or shape. If not specified, we rely on dims and
         # finally, observed, to determine the shape of the variable.
-        if not ("size" in kwargs or "shape" in kwargs):
+        if kwargs.get("size") is None and kwargs.get("shape") is None:
             if dims is not None:
                 kwargs["shape"] = shape_from_dims(dims, model)
             elif observed is not None:

pymc/tests/distributions/test_shape_utils.py

@@ -218,7 +218,7 @@ def test_broadcast_dist_samples_to(self, samples_to_broadcast_to):
                 broadcast_dist_samples_to(to_shape, samples, size=size)
 
 
-class TestShapeDimsSize:
+class TestSizeShapeDimsObserved:
     @pytest.mark.parametrize("param_shape", [(), (2,)])
     @pytest.mark.parametrize("batch_shape", [(), (3,)])
     @pytest.mark.parametrize(
@@ -465,6 +465,13 @@ def test_size_from_observed_rng_update(self):
         # draw, would match the first value of the second draw
         assert fn()[1] != fn()[0]
 
+    def test_explicit_size_shape_none(self):
+        with pm.Model() as m:
+            x = pm.Normal("x", shape=None, observed=[1, 2, 3])
+            y = pm.Normal("y", size=None, observed=[1, 2, 3, 4])
+        assert x.shape.eval().item() == 3
+        assert y.shape.eval().item() == 4
+
 
 def test_rv_size_is_none():
     rv = pm.Normal.dist(0, 1, size=None)