Symbolic trace error about torch.nn.functional.pad
Labels: triaged, module: fx

### 🐛 Describe the bug

Symbolic trace failed with `torch.nn.functional.pad`.

I found a similar issue here https://github.com/pytorch/vision/issues/6166. That exact issue have been fixed, but if we modify the code snippet  as following, the error still exists.

```python
import torch
import torch.fx
import torch.nn.functional as F

class CustomModule(torch.nn.Module):
    def forward(self, x):
        bs, c, h, w = x.shape
        return F.pad(x, (w, h))

m = CustomModule()
x = torch.rand(1, 3, 4, 4)
m_fx = torch.fx.symbolic_trace(m)
```

```
TypeError: pad(): argument 'pad' (position 2) must be tuple of ints, not tuple
```

It seems that if there is not any straight interger value in the padding sizes, symbolic trace will be not able to correctly parse the params.

### Versions

PyTorch version: 1.13.0+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: CentOS Linux 7 (Core) (x86_64)
GCC version: (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5)
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.17

Python version: 3.8.15 (default, Nov 24 2022, 15:19:38)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.6.55
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.76
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
CPU(s):                96
On-line CPU(s) list:   0-95
Thread(s) per core:    2
Core(s) per socket:    24

Versions of relevant libraries:
[pip3] flake8==3.9.2
[pip3] flake8-bugbear==21.4.3
[pip3] flake8-comprehensions==3.6.0
[pip3] flake8-polyfill==1.0.2
[pip3] flake8-tidy-imports==4.5.0
[pip3] horizon-plugin-pytorch==1.7.1.dev20230602+cu116.torch1130.f854a
[pip3] msgpack-numpy==0.4.8
[pip3] mypy==0.910
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.5
[pip3] pytorch-crf==0.7.2
[pip3] pytorch3d==0.7.2
[pip3] torch==1.13.0+cu116
[pip3] torchaudio==0.13.0+cu116
[pip3] torchmetrics==0.5.0
[pip3] torchvision==0.14.0+cu116
[conda] blas                      1.0                         mkl
[conda] mkl                       2023.1.0         h6d00ec8_46342
[conda] mkl-service               2.4.0            py38h5eee18b_1
[conda] mkl_fft                   1.3.6            py38h417a72b_1
[conda] mkl_random                1.2.2            py38h417a72b_1
[conda] msgpack-numpy             0.4.8                    pypi_0    pypi
[conda] numpy                     1.19.5                   pypi_0    pypi
[conda] numpy-base                1.23.5           py38h060ed82_1
[conda] pytorch-crf               0.7.2                    pypi_0    pypi
[conda] pytorch3d                 0.7.2                    pypi_0    pypi
[conda] torch                     1.13.0+cu116             pypi_0    pypi
[conda] torchaudio                0.13.0+cu116             pypi_0    pypi
[conda] torchmetrics              0.5.0                    pypi_0    pypi
[conda] torchvision               0.14.0+cu116             pypi_0    pypi

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv