tensor cuda memory is not released when using nested_tensor_from_jagged

### 🐛 Describe the bug

I found it's interesting that the cuda memory can not be released by gc when I used `nested_tensor_from_jagged`.

```python

import torch
import gc

def run():
a_torch = torch.empty([1152, 32, 32], dtype=torch.float32, device="cuda")
torch.nested.nested_tensor_from_jagged(
values=a_torch, offsets=torch.tensor([0, 512, 1152], device='cuda', dtype=torch.int32), jagged_dim=1
)

def test():
print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
print(f"Reserved: {torch.cuda.memory_reserved() / 1024**2:.2f} MB")
print(f"Max Allocated: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB")
print(f"Max Reserved: {torch.cuda.max_memory_reserved() / 1024**2:.2f} MB")
print("Running test...")

run()

torch.cuda.synchronize()
torch.cuda.empty_cache()
gc.collect()
print("Memory usage after test...")
print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
print(f"Reserved: {torch.cuda.memory_reserved() / 1024**2:.2f} MB")
print(f"Max Allocated: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB")
print(f"Max Reserved: {torch.cuda.max_memory_reserved() / 1024**2:.2f} MB")

if __name__ == "__main__":
test()
```

output
```
Allocated: 0.00 MB
Reserved: 0.00 MB
Max Allocated: 0.00 MB
Max Reserved: 0.00 MB
Running test...
W0310 11:56:42.483000 87824 site-packages/torch/fx/_symbolic_trace.py:52] is_fx_tracing will return true for both fx.symbolic_trace and torch.export. Please use is_fx_tracing_symbolic_tracing() for specifically fx.symbolic_trace or torch.compiler.is_compiling() for specifically torch.export/compile.
Memory usage after test...
Allocated: 4.50 MB
Reserved: 22.00 MB
Max Allocated: 4.50 MB
Max Reserved: 22.00 MB
```
Allocated memory in the end is expected to be 0.

### Versions

Collecting environment information...
PyTorch version: 2.9.0+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (aarch64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.14.0 (main, Feb 26 2026, 03:53:09) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-6.14.0-1013-nvidia-64k-aarch64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 13.1.66
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: NVIDIA GB200
GPU 1: NVIDIA GB200
GPU 2: NVIDIA GB200
GPU 3: NVIDIA GB200

Nvidia driver version: 580.82.07
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 144
On-line CPU(s) list: 0-143
Thread(s) per core: 1
Core(s) per socket: 72
Socket(s): 2
NUMA node(s): 34
Vendor ID: ARM
Model: 0
Stepping: r0p0
Frequency boost: disabled
CPU max MHz: 3438.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00
L1d cache: 9 MiB
L1i cache: 9 MiB
L2 cache: 144 MiB
L3 cache: 228 MiB
NUMA node0 CPU(s): 0-71
NUMA node1 CPU(s): 72-143
NUMA node2 CPU(s):
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Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Not affected
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Mitigation; CSV2, but not BHB
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti

Versions of relevant libraries:
[pip3] mypy==1.13.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.4.2
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.9.0+cu128
[pip3] torch_c_dlpack_ext==0.1.3
[pip3] triton==3.5.0
[conda] Could not collect

cc @ezyang @gchanan @kadeng @msaroufim @ptrblck @eqy @jerryzh168 @tinglvv @nWEIdia @albanD @cpuhrsch @jbschlosser @bhosmer @drisspg @soulitzer @davidberard98 @YuqingJ

Start by running the provided Python reproducer and measuring allocation around the nested_tensor_from_jagged entry point. Compare the behavior with and without this call, then trace the nested-tensor and CUDA ownership paths implicated by the reproduction. Done means the reproducer no longer leaves unexpected allocated memory after gc.collect() and torch.cuda.empty_cache().

Written by the indexing model from the issue text.