Cannot export MiVOLO model into `onnx` format using `torch.onnx.export`
Labels: module: onnx, triaged

### 🐛 Describe the bug

I am trying to convert a [pytorch based model](https://github.com/WildChlamydia/MiVOLO) into onnx format. The pytorch model is working fine, but I can't convert it into onnx. I am adding following code snippet [here](https://github.com/WildChlamydia/MiVOLO/blob/024d3ae71d42c4147bf21a46bbc2f7521cd94d53/mivolo/model/mi_volo.py#L123) to convert `self.model` into `onnx` format. Here is my code snippet
```python
        random_input = torch.randn(1, 6, 224, 224, device=self.device)
        onnx_model_name = "mi_volo.onnx"
        # pytorch to onnx
        torch.onnx.export(self.model, random_input, onnx_model_name, verbose=True, opset_version=18)
```

but I am getting the following error:
```
============= Diagnostic Run torch.onnx.export version 2.0.1+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================

Traceback (most recent call last):
  File "/home/master/.local/lib/python3.10/site-packages/torch/onnx/symbolic_opset18.py", line 52, in col2im
    num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
TypeError: 'NoneType' object is not subscriptable
```

I tried debugging the error, but couldn't understand it due to less familiarity with the conversion process. The actual line that is causing an error inside `torch/onnx/symbolic_opset18.py` is 
```python
num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
```
At the time of the error, I tried inspecting the `output_size` variable and it is 
```
1072 defined in (%1072 : int[] = prim::ListConstruct(%958, %963), scope: mivolo.model.mivolo_model.MiVOLOModel::/torch.nn.modules.container.Sequential::network.0/timm.models.volo.Outlooker::network.0.0/timm.models.volo.OutlookAttention::attn)
```
Debugging deeper, inside `_get_tensor_sizes` method, the [following branch](https://github.com/pytorch/pytorch/blob/dfd441a12cede6702d1fd160eb808778b62adae0/torch/onnx/symbolic_helper.py#L571C1-L572C20) is causing a `None` return.
```python
    if not _is_tensor(x) or x.type() is None:
        return None
```
**MiVOLO** - Latest Pull
**Pytorch version** - 2.0.1
**onnx version** - 1.14.1
**OS** - Ubuntu 22.04.3LTS

Any help/direction/discussion will be highly appreciated, thank you.

### Versions

Collecting environment information...
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
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 3.26.3
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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:                   39 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          4
On-line CPU(s) list:             0-3
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
CPU family:                      6
Model:                           142
Thread(s) per core:              2
Core(s) per socket:              2
Socket(s):                       1
Stepping:                        9
CPU max MHz:                     3100.0000
CPU min MHz:                     400.0000
BogoMIPS:                        5399.81
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust sgx bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       64 KiB (2 instances)
L1i cache:                       64 KiB (2 instances)
L2 cache:                        512 KiB (2 instances)
L3 cache:                        3 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-3
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Mitigation; Microcode
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.1
[pip3] torchaudio==0.13.1+cpu
[pip3] torchdata==0.6.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2
[pip3] triton==2.0.0
[conda] Could not collect