`1/torch.inf` produce inconsistent results
Labels: module: numerical-stability, triaged, module: complex, module: type promotion, module: edge cases

### 🐛 Describe the bug

```Python
import torch
def hf0(m, n):
    x0 = torch.ones(m,n,dtype=torch.complex128)
    x1 = torch.ones(m,n,dtype=torch.float64)
    x1[0,0] = torch.inf
    print(x0/x1)
hf0(1, 2)
# tensor([[0.+0.j, 1.+0.j]], dtype=torch.complex128)
hf0(2, 2)
# tensor([[nan+nanj, 1.+0.j],
#         [1.+0.j, 1.+0.j]], dtype=torch.complex128)
```

the following environments are tested

| environment | results |
| :-: | :-: |
| ubuntu 22.04 | `[0,1], [[nan,1],[1,1]]` |
| mac m1 silicon | `[0,1], [[0,1],[1,1]]` |
| google colab [link](https://colab.research.google.com/drive/1K30lY7zLvbrQ5Jl5lvNvWpF6cpQPNgcy?usp=sharing) | `[0,1], [[nan,1],[1,1]]` |


### Versions

Collecting environment information...
PyTorch version: 2.0.0.post200
Is debug build: False
CUDA used to build PyTorch: 11.2
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: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 535.86.10
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.3
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:                   48 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          16
On-line CPU(s) list:             0-15
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen 7 5800H with Radeon Graphics
CPU family:                      25
Model:                           80
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
Stepping:                        0
Frequency boost:                 enabled
CPU max MHz:                     4462.5000
CPU min MHz:                     1200.0000
BogoMIPS:                        6387.93
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 cppc 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
Virtualization:                  AMD-V
L1d cache:                       256 KiB (8 instances)
L1i cache:                       256 KiB (8 instances)
L2 cache:                        4 MiB (8 instances)
L3 cache:                        16 MiB (1 instance)
NUMA node(s):                    1
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
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

Versions of relevant libraries:
[pip3] numpy==1.25.1
[pip3] torch==2.0.0.post200
[pip3] torchvision==0.15.2a0+072ec57
[conda] cudatoolkit               11.8.0              h4ba93d1_12    conda-forge
[conda] libmagma                  2.7.1                hc72dce7_3    conda-forge
[conda] libmagma_sparse           2.7.1                hc72dce7_4    conda-forge
[conda] magma                     2.7.1                ha770c72_4    conda-forge
[conda] mkl                       2022.2.1         h84fe81f_16997    conda-forge
[conda] numpy                     1.25.1          py310ha4c1d20_0    conda-forge
[conda] pytorch                   2.0.0           cuda112py310he33e0d6_200    conda-forge
[conda] torchvision               0.15.2          cuda112py310h0801bf5_1    conda-forge

cc @ezyang @anjali411 @dylanbespalko @mruberry @Lezcano @nikitaved @nairbv