Repository: pytorch
Issue: backwards compatibility about class _LRScheduler
Labels: triaged,module: LrScheduler

### 🐛 Describe the bug

In older version of pytorch, StepLR is a subclass of _LRScheduler, but in the latest version, StepLR is not. Therefore, the following previous code will not work as expected.

```python
from torch.optim.lr_scheduler import StepLR
scheduler = StepLR(opt, step_size=100, gamma=0.1)
if isinstance(lr_scheduler, torch.optim.lr_scheduler._LRScheduler)):
    do something
else:
    error
```

The backward compatibility code related in the latest version of pytorch :
```python
# Including _LRScheduler for backwards compatibility
# Subclass instead of assign because we want __name__ of _LRScheduler to be _LRScheduler (assigning would make it LRScheduler).
class _LRScheduler(LRScheduler):
    pass
```

### Versions

Collecting environment information...
PyTorch version: 2.0.1
Is debug build: False
CUDA used to build PyTorch: 11.7
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: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27

Python version: 3.11.4 (main, Jul  5 2023, 13:45:01) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-957.el7.x86_64-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.3.58
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.43.04
cuDNN version: Probably one of the following:
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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):              112
On-line CPU(s) list: 0-111
Thread(s) per core:  2
Core(s) per socket:  28
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz
Stepping:            6
CPU MHz:             3500.000
CPU max MHz:         3500.0000
CPU min MHz:         800.0000
BogoMIPS:            5200.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            43008K
NUMA node0 CPU(s):   0-27,56-83
NUMA node1 CPU(s):   28-55,84-111
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 aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 intel_pt ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq spec_ctrl intel_stibp flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.25.0
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[pip3] torchvision==0.15.2
[pip3] triton==2.0.0
[pip3] warmup-scheduler-pytorch==0.1.2