Repository: pytorch
Issue: torch.export.unflatten of a submodule used twice creates a strange and maybe incorrect graph
Labels: triaged,oncall: pt2,export-triaged

### 🐛 Describe the bug

```python
class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(1, 1,bias=False)

    def forward(self, x):
        return self.linear(self.linear(x))
with torch.no_grad():
    m=M()
    m.linear.weight[:]=10.
    x=torch.tensor([2.])
    exported= torch.export.export(m, (torch.ones(1),))
    unflattened = torch.export.unflatten(exported)
    print(unflattened.graph)
    print(unflattened.linear.graph)
    assert m(x)==200.0
    assert unflattened(x)==200.0
```

The code passes but it prints a strange graph:
```
graph():
    %x : [num_users=1] = placeholder[target=x]
    %linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
    return (linear,)
graph():
    %x : [num_users=1] = placeholder[target=x]
    %weight : [num_users=2] = get_attr[target=weight]
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %weight), kwargs = {})
    %linear_1 : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%linear, %weight), kwargs = {})
    return linear_1
```
linear is now not representing a `torch.nn.Linear` but something different - two calls to `torch.ops.aten.linear`. Its not a correctness issue in this case, but might be in other cases? 

Anyways, the modules are not "properly preserved" as I see it. We had a module that is a linear layer, and that module was called twice, now we have a module that is two linear layers that is called once.

### Versions

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.10.6 (main, Aug 30 2022, 16:00:07) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.91
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: NVIDIA A100-PCIE-40GB
GPU 1: NVIDIA A100-PCIE-40GB

Nvidia driver version: 550.54.14
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
Address sizes:                      43 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             64
On-line CPU(s) list:                0-63
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7502 32-Core Processor
CPU family:                         23
Model:                              49
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           4999.98
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
Virtualization:                     AMD-V
L1d cache:                          1 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           16 MiB (32 instances)
L3 cache:                           128 MiB (8 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-63
Vulnerability Gather data sampling: 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 Retbleed:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy==1.10.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.4
[pip3] torch==2.4.0
[pip3] torchaudio==2.4.0+cu121
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] No relevant packages

cc @ezyang @chauhang @penguinwu