Fix einsum doctest

pytensor/tensor/einsum.py

@@ -86,28 +86,30 @@ def _iota(shape: TensorVariable, axis: int) -> TensorVariable:
 
     .. testcode::
 
-        import pytensor as pt
-        shape = pt.as_tensor('shape', (5,))
-        print(pt._iota(shape, 0).eval())
+        import pytensor.tensor as pt
+        from pytensor.tensor.einsum import _iota
+
+        shape = pt.as_tensor((5,))
+        print(_iota(shape, 0).eval())
 
     .. testoutput::
 
-        [0., 1., 2., 3., 4.]
+         [0 1 2 3 4]
 
-    In higher dimensions, it will look like many concatenated `pt.arange`:
+    In higher dimensions, it will look like many concatenated `arange`:
 
     .. testcode::
 
-        shape = pt.as_tensor('shape', (5, 5))
-        print(pt._iota(shape, 1).eval())
+        shape = pt.as_tensor((5, 5))
+        print(_iota(shape, 1).eval())
 
     .. testoutput::
 
-        [[0., 1., 2., 3., 4.],
-        [0., 1., 2., 3., 4.],
-        [0., 1., 2., 3., 4.],
-        [0., 1., 2., 3., 4.],
-        [0., 1., 2., 3., 4.]]
+        [[0 1 2 3 4]
+         [0 1 2 3 4]
+         [0 1 2 3 4]
+         [0 1 2 3 4]
+         [0 1 2 3 4]]
 
     Setting ``axis=0`` above would result in the transpose of the output.
     """
@@ -218,14 +220,14 @@ def _general_dot(
         from pytensor.tensor.einsum import _general_dot
         import numpy as np
 
-        A = pt.tensor(shape = (3, 4, 5))
-        B = pt.tensor(shape = (3, 5, 2))
+        A = pt.tensor(shape=(3, 4, 5))
+        B = pt.tensor(shape=(3, 5, 2))
 
         result = _general_dot((A, B), axes=[[2], [1]], batch_axes=[[0], [0]])
 
         A_val = np.empty((3, 4, 5))
         B_val = np.empty((3, 5, 2))
-        print(result.shape.eval({A:A_val, B:B_val}))
+        print(tuple(result.shape.eval({A:A_val, B:B_val})))
 
     .. testoutput::