Torch.fx.symbolic_trace removes some of the keys from module state_dict
Labels: triaged, module: fx

### 🐛 Describe the bug

I’m doing some graph editing using torch.fx and timm liblary.
I was editing already resnet18 and it was completely successful.
But when using mobilenet I run into problem cause obtained graph doesn’t have some keys already that our original model had.
As an outcome of that our orignal model is fitting good (as supposed) but the obtained graph is not fitting.
Example

```
from torch import fx
import timm

model = timm.create_model("mobilenetv2_035", num_classes=10)
fx_module = fx.symbolic_trace(model)

print(len(model.state_dict().keys()))
print(len(fx_module.state_dict().keys()))
print(set(model.state_dict().keys()).difference(fx_module.state_dict().keys()))

```

```
314
262
{'blocks.1.0.bn3.num_batches_tracked', 'blocks.3.0.bn3.num_batches_tracked', 'blocks.2.2.bn2.num_batches_tracked', 'blocks.5.1.bn3.num_batches_tracked', 'blocks.3.2.bn1.num_batches_tracked', 'blocks.4.2.bn3.num_batches_tracked', 'blocks.2.0.bn3.num_batches_tracked', 'blocks.5.1.bn2.num_batches_tracked', 'blocks.4.2.bn2.num_batches_tracked', 'blocks.4.1.bn1.num_batches_tracked', 'blocks.3.1.bn3.num_batches_tracked', 'blocks.1.1.bn3.num_batches_tracked', 'bn1.num_batches_tracked', 'blocks.4.2.bn1.num_batches_tracked', 'bn2.num_batches_tracked', 'blocks.4.0.bn1.num_batches_tracked', 'blocks.4.0.bn3.num_batches_tracked', 'blocks.5.1.bn1.num_batches_tracked', 'blocks.4.1.bn2.num_batches_tracked', 'blocks.4.0.bn2.num_batches_tracked', 'blocks.5.0.bn1.num_batches_tracked', 'blocks.5.2.bn1.num_batches_tracked', 'blocks.3.3.bn2.num_batches_tracked', 'blocks.5.2.bn3.num_batches_tracked', 'blocks.2.2.bn1.num_batches_tracked', 'blocks.5.0.bn3.num_batches_tracked', 'blocks.3.1.bn1.num_batches_tracked', 'blocks.2.1.bn1.num_batches_tracked', 'blocks.4.1.bn3.num_batches_tracked', 'blocks.2.2.bn3.num_batches_tracked', 'blocks.0.0.bn2.num_batches_tracked', 'blocks.3.3.bn1.num_batches_tracked', 'blocks.3.2.bn3.num_batches_tracked', 'blocks.0.0.bn1.num_batches_tracked', 'blocks.1.1.bn1.num_batches_tracked', 'blocks.3.2.bn2.num_batches_tracked', 'blocks.3.1.bn2.num_batches_tracked', 'blocks.2.0.bn1.num_batches_tracked', 'blocks.3.0.bn2.num_batches_tracked', 'blocks.2.1.bn3.num_batches_tracked', 'blocks.1.1.bn2.num_batches_tracked', 'blocks.3.0.bn1.num_batches_tracked', 'blocks.6.0.bn3.num_batches_tracked', 'blocks.5.0.bn2.num_batches_tracked', 'blocks.2.0.bn2.num_batches_tracked', 'blocks.6.0.bn1.num_batches_tracked', 'blocks.2.1.bn2.num_batches_tracked', 'blocks.6.0.bn2.num_batches_tracked', 'blocks.1.0.bn2.num_batches_tracked', 'blocks.3.3.bn3.num_batches_tracked', 'blocks.5.2.bn2.num_batches_tracked', 'blocks.1.0.bn1.num_batches_tracked'}

```


### Versions

Collecting environment information...
PyTorch version: 2.0.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.31

Python version: 3.8.10 (default, Mar 13 2023, 10:26:41)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 530.30.02
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
Address sizes:                   48 bits physical, 48 bits virtual
CPU(s):                          16
On-line CPU(s) list:             0-15
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           33
Model name:                      AMD Ryzen 7 5800X 8-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         2200.000
CPU max MHz:                     3800,0000
CPU min MHz:                     2200,0000
BogoMIPS:                        7585.45
Virtualization:                  AMD-V
L1d cache:                       256 KiB
L1i cache:                       256 KiB
L2 cache:                        4 MiB
L3 cache:                        32 MiB
NUMA node0 CPU(s):               0-15
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:          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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 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 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm

Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] flamingo-pytorch==0.1.2
[pip3] lion-pytorch==0.0.7
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.5
[pip3] pytorch-ignite==0.4.8
[pip3] pytorch-lightning==1.7.0
[pip3] pytorch-metric-learning==1.6.3
[pip3] segmentation-models-pytorch==0.2.1
[pip3] torch==2.0.0+cu118
[pip3] torch-audiomentations==0.11.0
[pip3] torch-pitch-shift==1.2.2
[pip3] torchaudio==0.9.0
[pip3] torchmetrics==0.10.2
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.15.1+cu118
[pip3] vit-pytorch==1.2.0
[conda] Could not collect


cc @ezyang @SherlockNoMad @soumith @EikanWang @jgong5 @wenzhe-nrv