Fix numpy dtype conversion in TensorType

TensorType.dtype must be a string, so the code
has been changed from `self.dtype = np.dtype(dtype).type`,
where the right-hand side is of type `np.generic`, to
`self.dtype = str(np.dtype(dtype))`, where the right-hand
side is a string that satisfies:

`self.dtype == str(np.dtype(self.dtype))`

This doesn't change the behavior of `np.array(..., dtype=self.dtype)`
etc.

Author: brendan-m-murphy

Diff for: pytensor/tensor/type.py

@@ -4,6 +4,7 @@
 from typing import TYPE_CHECKING, Literal, Optional
 
 import numpy as np
+import numpy.typing as npt
 
 import pytensor
 from pytensor import scalar as ps
@@ -69,7 +70,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
 
     def __init__(
         self,
-        dtype: str | np.dtype,
+        dtype: str | npt.DTypeLike,
         shape: Iterable[bool | int | None] | None = None,
         name: str | None = None,
         broadcastable: Iterable[bool] | None = None,
@@ -101,11 +102,11 @@ def __init__(
         if str(dtype) == "floatX":
             self.dtype = config.floatX
         else:
-            if np.dtype(dtype).type is None:
+            try:
+                self.dtype = str(np.dtype(dtype))
+            except TypeError:
                 raise TypeError(f"Invalid dtype: {dtype}")
 
-            self.dtype = np.dtype(dtype).name
-
         def parse_bcast_and_shape(s):
             if isinstance(s, bool | np.bool_):
                 return 1 if s else None