INTERNAL ASSERT FAILED for _jit_pass_vulkan_optimize_for_mobile (Google Colab)
Labels: oncall: jit, oncall: mobile, module: vulkan

### 🐛 Describe the bug

Running into an issue with the mobile_optimizer when using Vulkan as the backend.

```
elif backend == 'vulkan':
---> 67         optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(script_module._c, preserved_methods_str)
```

Error Message: 
```RuntimeError: 0 INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1662793958506/work/torch/csrc/jit/ir/alias_analysis.cpp":608, please report a bug to PyTorch. We don't have an op for vulkan_prepack::create_conv2d_context but it isn't a special case.  Argument types: Tensor, Tensor, int[], int[], int[], int, NoneType, NoneType, ```


### Versions

Collecting environment information...
PyTorch version: 1.13.0.dev20220910
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.22.6
Libc version: glibc-2.17

Python version: 3.7.13 (default, Mar 29 2022, 02:18:16)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.10.133+-x86_64-with-debian-buster-sid
Is CUDA available: True
CUDA runtime version: 11.1.105
GPU models and configuration: GPU 0: Tesla P100-PCIE-16GB
Nvidia driver version: 460.32.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.0.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.0.5
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] pytorch-lightning==1.6.0.dev0
[pip3] torch==1.13.0.dev20220910
[pip3] torchmetrics==0.9.3
[pip3] torchtext==0.14.0.dev20220910
[pip3] torchvision==0.14.0.dev20220910
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py37h7f8727e_0  
[conda] mkl_fft                   1.3.1            py37hd3c417c_0  
[conda] mkl_random                1.2.2            py37h51133e4_0  
[conda] numpy                     1.21.5           py37he7a7128_2  
[conda] numpy-base                1.21.5           py37hf524024_2  
[conda] pytorch                   1.13.0.dev20220910 py3.7_cuda11.3_cudnn8.3.2_0    pytorch-nightly
[conda] pytorch-lightning         1.6.0.dev0               pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch-nightly
[conda] torchmetrics              0.9.3                    pypi_0    pypi
[conda] torchtext                 0.14.0.dev20220910            py37    pytorch-nightly
[conda] torchvision               0.14.0.dev20220910      py37_cu113    pytorch-nightly