Remove unnecessary exception arguments from TensorType.filter

aesara/tensor/type.py

@@ -205,33 +205,18 @@ class TensorType(CType):
                 f" got {data.ndim} with shape {data.shape}."
             )
         if not data.flags.aligned:
-            try:
-                msg = "object buffer" + str(data.data)
-            except AttributeError:
-                msg = ""
             raise TypeError(
                 "The numpy.ndarray object is not aligned."
                 " Aesara C code does not support that.",
-                msg,
-                "object shape",
-                data.shape,
-                "object strides",
-                data.strides,
-                "object dtype",
-                data.dtype,
             )
 
         i = 0
         for b in self.broadcastable:
             if b and data.shape[i] != 1:
-                raise TypeError(
-                    "Non-unit value on shape on a broadcastable" " dimension.",
-                    data.shape,
-                    self.broadcastable,
-                )
+                raise TypeError("Non-unit value on shape on a broadcastable dimension.")
             i += 1
         if self.filter_checks_isfinite and not np.all(np.isfinite(data)):
-            raise ValueError("non-finite elements not allowed")
+            raise ValueError("Non-finite elements not allowed")
         return data
 
     def filter_variable(self, other, allow_convert=True):

tests/tensor/test_type.py

@@ -19,6 +19,20 @@ def test_filter_variable():
     with pytest.raises(TypeError):
         test_type.filter(test_type())
 
+    test_type = TensorType(config.floatX, [True, False])
+
+    with pytest.raises(TypeError):
+        test_type.filter(np.empty((0, 1), dtype=config.floatX))
+
+    with pytest.raises(TypeError, match=".*not aligned.*"):
+        test_val = np.empty((1, 2), dtype=config.floatX)
+        test_val.flags.aligned = False
+        test_type.filter(test_val)
+
+    with pytest.raises(ValueError, match="Non-finite"):
+        test_type.filter_checks_isfinite = True
+        test_type.filter(np.full((1, 2), np.inf, dtype=config.floatX))
+
 
 def test_filter_strict():
     test_type = TensorType(config.floatX, [])