Warn when more than N recompile is triggered.

Nobody has claimed this yet.

```python
import thunder
import torch

def foo(x, y):
return x + y

tfoo = thunder.jit(foo)
tcfoo = torch.compile(foo, dynamic=False)

x = torch.randn(10)

for i in range(10):
tfoo(x, i)
tcfoo(x, i)

print("Cache Misses:", thunder.cache_misses(tfoo))

```

Output
```python
W1021 10:44:18.989000 2926727 torch/_dynamo/convert_frame.py:1355] [0/8] torch._dynamo hit config.recompile_limit (8)
W1021 10:44:18.989000 2926727 torch/_dynamo/convert_frame.py:1355] [0/8] function: 'foo' (/opt/pytorch/lightning-thunder/test_thunder_recompile.py:4)
W1021 10:44:18.989000 2926727 torch/_dynamo/convert_frame.py:1355] [0/8] last reason: 0/7: y == 7 # return x + y # pt/pytorch/lightning-thunder/test_thunder_recompile.py:5 in foo
W1021 10:44:18.989000 2926727 torch/_dynamo/convert_frame.py:1355] [0/8] To log all recompilation reasons, use TORCH_LOGS="recompiles".
W1021 10:44:18.989000 2926727 torch/_dynamo/convert_frame.py:1355] [0/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html

Cache Misses: 10
```

It would be nice if thunder will also warn when there are a lot of recompiles.

Start by running the provided Python example with thunder.jit, torch.compile, and thunder.cache_misses to reproduce the ten cache misses and compare the output with PyTorch's recompilation warning. Trace how Thunder counts cache misses, then determine where a warning for exceeding a configurable N should be emitted; done means excessive recompilation produces a clear warning.

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