Repository: pytorch
Issue: `torch._numpy.ndarray.astype()` does not accept Numpy Dtypes correctly
Labels: triaged,module: numpy

### 🐛 Describe the bug

Running

```python
import torch._numpy as xp
import numpy as np
x = xp.arange(4)
y = x.astype(xp.float32)
z = x.astype(np.float32)
```

raises 

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/sese6502/environments/finest/lib/python3.12/site-packages/torch/_numpy/_ndarray.py", line 307, in astype
    torch_dtype = _dtypes.dtype(dtype).torch_dtype
                  ^^^^^^^^^^^^^^^^^^^^
  File "/home/sese6502/environments/finest/lib/python3.12/site-packages/torch/_numpy/_dtypes.py", line 271, in dtype
    return DType(arg)
           ^^^^^^^^^^
  File "/home/sese6502/environments/finest/lib/python3.12/site-packages/torch/_numpy/_dtypes.py", line 289, in __init__
    sctype = arg.dtype._scalar_type
             ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'getset_descriptor' object has no attribute '_scalar_type'
```

in the last line. 

EDIT: Similar things happen, if I call `torch._numpy.empty((1,1), dtype=np.float32)`. The code basically fails at the same location.

However,

```
import cupy as xp
import numpy as np
x = xp.arange(4)
y = x.astype(np.float32)
z = x.astype(xp.float32)
```

works without issues. The same hold true for `import dask as xp`.

So I would expect that the numpy backend of torch also behaves nicely in this moment.


### Versions

My output of `collect_env.py` is

```
Collecting environment information...
PyTorch version: 2.4.1.post3
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: AlmaLinux release 8.9 (Midnight Oncilla) (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-20)
Clang version: Could not collect
CMake version: version 3.26.5
Libc version: glibc-2.28

Python version: 3.12.7 | packaged by conda-forge | (main, Oct  4 2024, 16:05:46) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-513.18.1.el8_9.x86_64-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S

Nvidia driver version: 550.54.15
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              32
On-line CPU(s) list: 0-31
Thread(s) per core:  1
Core(s) per socket:  16
Socket(s):           2
NUMA node(s):        2
Vendor ID:           AuthenticAMD
CPU family:          25
Model:               1
Model name:          AMD EPYC 7343 16-Core Processor
Stepping:            1
CPU MHz:             3200.000
CPU max MHz:         3940.6250
CPU min MHz:         1500.0000
BogoMIPS:            6400.38
Virtualization:      AMD-V
L1d cache:           32K
L1i cache:           32K
L2 cache:            512K
L3 cache:            32768K
NUMA node0 CPU(s):   0-15
NUMA node1 CPU(s):   16-31
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr $

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==2.1.2
[pip3] torch==2.4.1.post3
[conda] libtorch                  2.4.1           cpu_generic_h169fe36_3    conda-forge
[conda] nomkl                     1.0                  h5ca1d4c_0    conda-forge
[conda] numpy                     2.1.2           py312h58c1407_0    conda-forge
[conda] pytorch                   2.4.1           cpu_generic_py312h2b7556c_3    conda-forge
```

cc @mruberry @rgommers