Schedule in PyTorchProfiler doesn't work

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## 🐛 Bug

The class `pytorch_lightning.profiler.PyTorchProfiler` doesn't work correctly with `schedule` parameter. It doesn't take into account the `repeat` parameter `torch.profiler.schedule`, which is very important in long-term learning.

### To Reproduce

Reproduce with the BoringModel:

UPD: new link with example of main incorrect work and work with commit changes:

https://colab.research.google.com/drive/1UbbLx5N5Th0MsXu1olQwWqo7QLGd-lRY?usp=sharing

### Expected behavior

I used `pytorch_lightning.profiler.PyTorchProfiler` with `schedule=torch.profiler.schedule(wait=2, warmup=1, active=3, repeat=5,)`. And according to [torch docs](https://pytorch.org/docs/stable/profiler.html#torch.profiler.schedule), i expect that there will be 5 cycles, each of which consists of 2 wait + 1 warmup + 3 active = 6 steps (per cycle), but in fact `PyTorchProfiler` records information about fewer cycles.

In code terms:
```
profiler = PyTorchProfiler(
schedule=torch.profiler.schedule(
wait=2,
warmup=1,
active=3,
repeat=5,
),
)

model = BoringModel()
trainer = Trainer(
max_epochs=1,
profiler=profiler,
)
trainer.fit(model, train_dataloaders=train_data)
```
in this case the profiler should return information about 15 steps (3 active * 5 cylcles) to me, however it returns information about fewer steps because it doesn't record some cycles.

### Environment

```
* CUDA:
- GPU:
- Tesla T4
- available: True
- version: 11.3
* Packages:
- lightning: None
- lightning_app: None
- numpy: 1.21.6
- pyTorch_debug: False
- pyTorch_version: 1.12.0+cu113
- pytorch-lightning: 1.7.0
- tqdm: 4.64.0
* System:
- OS: Linux
- architecture:
- 64bit
-
- processor: x86_64
- python: 3.7.13
- version: #1 SMP Sun Apr 24 10:03:06 PDT 2022
```

### Additional context

I found the same issue #12611 .

cc @carmocca @kaushikb11 @ninginthecloud @rohitgr7 @nbcsm @guotuofeng

Start at the PyTorchProfiler class and compare its handling of torch.profiler.schedule with the linked PyTorch documentation. Reproduce the behavior using the BoringModel and the provided Colab example with repeat=5. Done means all five wait/warmup/active cycles are accounted for and the profiler reports the expected 15 active steps.

Written by the indexing model from the issue text.