Short-circuit `as_scalar` common cases faster

pytensor/scalar/basic.py

@@ -987,25 +987,28 @@ def constant(x, name=None, dtype=None) -> ScalarConstant:
 
 
 def as_scalar(x: Any, name: str | None = None) -> ScalarVariable:
-    from pytensor.tensor.basic import scalar_from_tensor
-    from pytensor.tensor.type import TensorType
+    if isinstance(x, ScalarVariable):
+        return x
+
+    if isinstance(x, Variable):
+        from pytensor.tensor.basic import scalar_from_tensor
+        from pytensor.tensor.type import TensorType
+
+        if isinstance(x.type, TensorType) and x.type.ndim == 0:
+            return scalar_from_tensor(x)
+        else:
+            raise TypeError(f"Cannot convert {x} to a scalar type")
 
     if isinstance(x, Apply):
+        # FIXME: Why do we support calling this with Apply?
+        #  Also, if we do, why can't we support multiple outputs?
         if len(x.outputs) != 1:
             raise ValueError(
                 "It is ambiguous which output of a multi-output"
                 " Op has to be fetched.",
                 x,
             )
-        else:
-            x = x.outputs[0]
-    if isinstance(x, Variable):
-        if isinstance(x, ScalarVariable):
-            return x
-        elif isinstance(x.type, TensorType) and x.type.ndim == 0:
-            return scalar_from_tensor(x)
-        else:
-            raise TypeError(f"Cannot convert {x} to a scalar type")
+        return as_scalar(x.outputs[0])
 
     return constant(x)