Repository: pytorch
Issue: `ReduceLROnPlateau` does not call `super().__init__()`
Labels: triaged,module: LrScheduler

### 🐛 Describe the bug

[`ReduceLROnPlateau.__init__`](https://github.com/pytorch/pytorch/blob/main/torch/optim/lr_scheduler.py#L1014-L1050) does not call `super().__init__(optimizer, last_epoch, verbose)`, leading to behavior inconsistent with the other schedulers.

I am aware that `ReduceLROnPlateau` was only recently updated to inherit from `LRScheduler` (https://github.com/pytorch/pytorch/issues/106767). This update did not include a `super().__init__(optimizer, last_epoch, verbose)` call, and thus some functionality does not occur:

```python
from torch import nn
from torch.optim import SGD, ReduceLROnPlateau

model = nn.Linear(8, 2)
opt = torch.optim.SGD(model.parameters(), lr=1e-3)
linear_lr = LinearLR(opt)
reduce_lr = ReduceLROnPlateau(opt)

print(linear_lr.base_lrs)
>> [0.001]
print(linear_lr._step_count)
>> 1

print(reduce_lr.base_lrs)
>> AttributeError: 'ReduceLROnPlateau' object has no attribute 'base_lrs'
print(reduce_lr._step_count)
>> AttributeError: 'ReduceLROnPlateau' object has no attribute '_step_count'
```

### Versions

Collecting environment information...
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.31

Python version: 3.8.17 (default, Jul  5 2023, 21:04:15)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA RTX A5000
GPU 1: NVIDIA RTX A5000
GPU 2: NVIDIA RTX A5000
GPU 3: NVIDIA RTX A5000
GPU 4: NVIDIA RTX A5000
GPU 5: NVIDIA RTX A5000
GPU 6: NVIDIA RTX A5000
GPU 7: NVIDIA RTX A5000

Nvidia driver version: 535.113.01
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
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             256
On-line CPU(s) list:                0-254
Off-line CPU(s) list:               255
Thread(s) per core:                 1
Core(s) per socket:                 64
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7763 64-Core Processor
Stepping:                           1
Frequency boost:                    enabled
CPU MHz:                            1536.310
CPU max MHz:                        2450.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           4900.17
Virtualization:                     AMD-V
L1d cache:                          2 MiB
L1i cache:                          2 MiB
L2 cache:                           32 MiB
L3 cache:                           256 MiB
NUMA node0 CPU(s):                  0-63,128-191
NUMA node1 CPU(s):                  64-127,192-254
Vulnerability Gather data sampling: Not affected
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 and seccomp
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
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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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 invpcid_single 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 wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] pytorch-metric-learning==2.3.0
[pip3] torch==2.2.1
[pip3] torchsampler==0.1.2
[pip3] torchvision==0.17.1
[pip3] torchviz==0.0.2
[pip3] triton==2.2.0
[conda] blas                      1.0                         mkl  
[conda] faiss-cpu                 1.7.4           py3.8_h8c27c75_0_cpu    pytorch
[conda] libfaiss                  1.7.4            h2bc3f7f_0_cpu    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.1            py38hd3c417c_0  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] numpy-base                1.24.3           py38h31eccc5_0  
[conda] pytorch-metric-learning   2.3.0                    pypi_0    pypi
[conda] torch                     2.2.1                    pypi_0    pypi
[conda] torchsampler              0.1.2                    pypi_0    pypi
[conda] torchvision               0.17.1                   pypi_0    pypi
[conda] torchviz                  0.0.2                    pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi