Infinite hang after running `Trainer.fit`

Nobody has claimed this yet.

### Bug description

I followed [this](https://curiousily.com/posts/multi-label-text-classification-with-bert-and-pytorch-lightning/) tutorial to build a lightning model for multi-label text classification, except I'm using my own dataset.

I had to fix some of the code because it was using deprecated syntax/features I believe.

However when I run `Trainer.fit` I get an infinite hang with no previous meaningful error.
As I'm not sure how to go about debugging this I'm creating this issue.

I'm running lightning 2.0.8, python 3.8.10, cuda 11.7 and nvidia driver 470.199.02.

### What version are you seeing the problem on?

v2.0

### How to reproduce the bug

```python
This is my custom defined module for training the model.

class Tagger(pl.LightningModule):

def __init__(self, n_classes: int, n_training_steps=None, n_warmup_steps=None):
super().__init__()
self.bert = BertModel.from_pretrained(bert_model_name, return_dict=True)
self.classifier = nn.Linear(self.bert.config.hidden_size, n_classes)
self.n_training_steps = n_training_steps
self.n_warmup_steps = n_warmup_steps
self.critireion = nn.BCELoss()

def forward(self, input_ids, attention_mask, labels=None):
output = self.bert(input_ids, attention_mask=attention_mask)
output = self.classifier(output.pooler_output)
output = torch.sigmoid(output)
loss = 0
if labels is not None:
loss = self.criterion(output, labels)
return loss, output

def training_step(self, batch, batch_idx):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
labels = batch['labels']
loss, outputs = self(input_ids, attention_mask, labels)
self.log('train_loss', loss, prog_bar=True, logger=True)
return {'loss': loss, 'predictions': outputs, 'labels': labels}

def validation_step(self, batch, batch_idx):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
labels = batch['labels']
loss, outputs = self(input_ids, attention_mask, labels)
self.log('val_loss', loss, prog_bar=True, logger=True)
return loss

def test_step(self, batch, batch_idx):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
labels = batch['labels']
loss, outputs = self(input_ids, attention_mask, labels)
self.log('test_loss', loss, prog_bar=True, logger=True)
return loss

def on_train_epoch_end(self, outputs):
labels = []
predictions = []
for output in outputs:
for out_labels in output['labels'].detach.cpu():
labels.append(out_labels)
for out_predictions in output['predictions'].detach().cpu():
predictions.append(out_predictions)
labels = torch.stack(labels).int()
predictions = torch.stack(predictions)
for i, name in enumerate(label_columns):
class_roc_auc = auroc(predictions[:, i], labels[:, i])
self.logger.experiment.add_scalar(f'{name}_roc_auc/Train', class_roc_auc, self.current_epoch)

def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=1e-5)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=self.n_warmup_steps,
num_training_steps=self.n_training_steps
)
return dict(optimizer=optimizer, lr_scheduler=scheduler, interval='step')
```

### Error messages and logs

This is what I get before it hangs.
```
[rank: 0] Global seed set to 42
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2
[rank: 1] Global seed set to 42
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2
Missing logger folder: model/saved/lightning_logs
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 2 processes
----------------------------------------------------------------------------------------------------

Missing logger folder: model/saved/lightning_logs
```

### Environment

Current environment

```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```

### More info

_No response_

cc @justusschock @awaelchli

Start with the provided custom Tagger module and the Trainer.fit entry point, then reproduce the two-process run using the reported Lightning, Python, CUDA, and driver versions. The distributed initialization logs are the only diagnostic evidence; done would require isolating a minimal reproducible hang and identifying a project-side fix or documenting why it is user-code or environment-specific.

Written by the indexing model from the issue text.