torch.compile Inductor crashes on onnx2torch-converted Add-Sub-Reshape model with FakeTensor shape input

### 🐛 Describe the bug

`torch.compile` with the Inductor backend crashes on an ONNX model converted by `onnx2torch`.

ONNX Runtime and eager `onnx2torch` both run correctly, but `torch.compile` crashes during tracing with a `FakeTensor` shape-related error.

```
#!/usr/bin/env python3
import sys
import numpy as np
import onnx
import onnxruntime as ort
import onnx2torch
import torch
from onnx import helper as oh, TensorProto as TP, numpy_helper as onh

np.random.seed(42)

x = np.random.randn(4, 64).astype(np.float32)
s = np.array([4, 64], dtype=np.int64)

graph = oh.make_graph(
[
oh.make_node("Add", ["X", "X"], ["add1"]),
oh.make_node("Add", ["X", "X"], ["add2"]),
oh.make_node("Sub", ["add1", "add2"], ["sub"]),
oh.make_node("Reshape", ["sub", "s"], ["Y"]),
],
"add_self_sub_double",
[oh.make_tensor_value_info("X", TP.FLOAT, [4, 64])],
[oh.make_tensor_value_info("Y", TP.FLOAT, [4, 64])],
initializer=[onh.from_array(s, "s")],
)

model = oh.make_model(graph, opset_imports=[oh.make_opsetid("", 13)])
model.ir_version = 8
mb = model.SerializeToString()

so = ort.SessionOptions()
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL

ort_out = ort.InferenceSession(
mb,
sess_options=so,
providers=["CPUExecutionProvider"],
).run(None, {"X": x})[0]

print(f"ORT: first4={ort_out.ravel()[:4]} max|y|={np.abs(ort_out).max():.2e} (should be 0)")

net = onnx2torch.convert(onnx.load_from_string(mb)).eval()
x_t = torch.from_numpy(x)

with torch.no_grad():
eager_out = net(x_t).numpy()

print(f"eager onnx2torch: first4={eager_out.ravel()[:4]} max|y|={np.abs(eager_out).max():.2e}")

try:
with torch.no_grad():
got = torch.compile(net, mode="default")(x_t).numpy()
diff = float(np.abs(got - eager_out).max())
print(f"torch.compile: first4={got.ravel()[:4]} max_diff_vs_eager={diff:.2e}")
except Exception as e:
print("BUG REPRODUCED: torch.compile crashes while eager succeeds")
print(f" {type(e).__name__}: {str(e)[:160]}")
sys.exit(0)

print("NOT REPRODUCED")
sys.exit(1)
```

### Error logs

```
ORT: first4=[0. 0. 0. 0.] max|y|=0.00e+00 (should be 0)
eager onnx2torch: first4=[0. 0. 0. 0.] max|y|=0.00e+00

BUG REPRODUCED: torch.compile crashes while eager succeeds
InternalTorchDynamoError: TypeError: torch.Size() takes an iterable of 'int' (item 0 is 'FakeTensor')
```

### Versions

```
PyTorch version: 2.11.0+cu130
Is debug build: False
CUDA used to build PyTorch: 13.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.2 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39

Python version: 3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU
Nvidia driver version: 545.92
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: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 16
On-line CPU(s) list: 0-15
Vendor ID: GenuineIntel
Model name: 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz
CPU family: 6
Model: 141
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
Stepping: 1
BogoMIPS: 4607.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid movdiri movdir64b fsrm avx512_vp2intersect md_clear flush_l1d arch_capabilities
Virtualization: VT-x
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 384 KiB (8 instances)
L1i cache: 256 KiB (8 instances)
L2 cache: 10 MiB (8 instances)
L3 cache: 24 MiB (1 instance)
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
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: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvtx==13.0.85
[pip3] onnx==1.21.0
[pip3] onnx2torch==1.5.15
[pip3] onnxruntime==1.23.2
[pip3] optree==0.19.1
[pip3] torch==2.11.0
[pip3] torchvision==0.26.0
[pip3] triton==3.6.0
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas 13.1.0.3 pypi_0 pypi
[conda] nvidia-cuda-cupti 13.0.85 pypi_0 pypi
[conda] nvidia-cuda-nvrtc 13.0.88 pypi_0 pypi
[conda] nvidia-cuda-runtime 13.0.96 pypi_0 pypi
[conda] nvidia-cudnn-cu13 9.19.0.56 pypi_0 pypi
[conda] nvidia-cufft 12.0.0.61 pypi_0 pypi
[conda] nvidia-curand 10.4.0.35 pypi_0 pypi
[conda] nvidia-cusolver 12.0.4.66 pypi_0 pypi
[conda] nvidia-cusparse 12.6.3.3 pypi_0 pypi
[conda] nvidia-cusparselt-cu13 0.8.0 pypi_0 pypi
[conda] nvidia-nccl-cu13 2.28.9 pypi_0 pypi
[conda] nvidia-nvjitlink 13.0.88 pypi_0 pypi
[conda] nvidia-nvtx 13.0.85 pypi_0 pypi
[conda] onnx2torch 1.5.15 pypi_0 pypi
[conda] optree 0.19.1 pypi_0 pypi
[conda] torch 2.11.0 pypi_0 pypi
[conda] torchvision 0.26.0 pypi_0 pypi
[conda] triton 3.6.0 pypi_0 pypi
```

cc @chauhang @penguinwu @eellison @aorenste @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @amjames @Lucaskabela @jataylo @azahed98 @bdhirsh @bobrenjc93