A gracefull design to introduce third-party models as tool for validation

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### Description & Motivation

python3.10.12 + pytorch_lightning 2.4.0
I need a gracefull design to introduce third-party pretrained models for use during the validation steps. so that there is no such Error reported:
```
RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, ....
```

### Pitch

I am training a model which need other third-party pretrained model during validation. example:
the third party model:
```
class PretrainedPicGen(torch.nn.Module):
def __init__(self, pretrained_path):
self.backbone = load_checkpoint(pretrained_path)
def forward(self, to_validate):
return self.backbone(to_validate)
```

And the lightning project I am training:
```
class MyModel(pl.LightningModule):
def __init__(self, my_param, third_party_pretrained_path):
....
self.pretrained_pic_gen = PretrainedPicGen(third_party_pretrained_path)
self.validation_outs = []
....
def validation_step(self, batch, *args, **kwargs):
validation_output = self.sample(....)
self.validation_outputs.append({"vali_out": validation_output})

def on_validation_epoch_end(self) : # Here we use the third party model for post processing the validation out
outputs = self.validation_outputs
for i, output in enumerate(outputs):
visible_output = self.pretrained_pic_gen(output)
self.logger.experiment.add_image(f"validate/{i}", visible_output, self.global_step)
```

and the config file yaml:
```
model:
class_path: myproject.MyModel
init_args:
my_param: 1234
third_party_pretrained_path: /path/to/third_party_pretrained
```

but When I run the training, there report the Error information as mentioned before:

```
RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, ....
```

And I think to config the `strategy=ddp_find_unused_parameters_true` may be not good solution, is there any gracefull design here? for example, support extra parameters in the `on_validation_epoch_end` callback and provide a gracefull third_party initialization supported in the config file.

### Alternatives

_No response_

### Additional context

_No response_

cc @borda @tchaton @justusschock @awaelchli

Start with the validation_step and on_validation_epoch_end entry points and the reported DDP unused-parameter error. Investigate how third-party models initialized from the YAML config participate in validation without affecting training parameters. Done would require an agreed design and implementation path that avoids requiring ddp_find_unused_parameters_true for this use case.

Written by the indexing model from the issue text.